JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI
JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI (JITI), untuk menyebarluaskan, mengembangkan dan menfasilitasi hasil penelitian inter-disiplin di bidang Teknologi Informasi dan Komunikasi, sistem komputer, informatika dan komunikasi sebagai media bagi para dosen, guru, peneliti dan para praktisi dalam bidang Teknologi Informasi dan Komunikasi, sistem komputer, informatika dan komunikasi dari seluruh Indonesia, dalam melakukan pertukaran informasi tentang hasil-hasil penelitian terbaru yang telah dilakukan. Jurnal ini terbit 1 tahun 4 kali (Januari, April, Juli dan Oktober). Jurnal ini terakreditasi SINTA 5 (SK Direktur Jenderal Pendidikan Tinggi, Riset, dan Teknologi Nomor 156/C/C3/KPT/2026 tanggal 7 April 2026, tentang Pemberitahuan Hasil Akreditasi Jurnal Ilmiah Periode 2 Tahun 2025), dimulai dari Volume 1 Nomor 3 Tahun 2023 sampai Volume 6 Nomor 2 Tahun 2028. Adapun ruang lingkup Jurnal adalah: Software Engineering (Rekayasa Perangkat Lunak) Information System (Sistem Informasi) Artificial Intelligence (Kecerdasan Buatan) Computer Based Learning (Pembelajaran Berbasis Komputer) Computer Networking & Data Communication (Jaringan Komputer & Komunikasi Data) Komunikasi Data Desain Komunikasi Visual Desain Multimedia
Perbandingan Motode Long Short-Term Memory (LSTM) dan Hybrid LSTM-XGBoost dalam Prediksi Harga Komoditas Sektor Pertanian di Sulawesi Tenggara
Agricultural commodity price instability remains a major challenge for farmers, fishers, and livestock breeders in Southeast Sulawesi Province, where 54,905 agricultural workers were still categorized as poor in 2023. Frequent price fluctuations complicate production and marketing decisions, creating the need for accurate price prediction systems. This study develops and compares two prediction models: a single Long Short-Term Memory (LSTM) model and a Hybrid LSTM-XGBoost model for 20 agricultural commodities across 17 regencies/cities in Southeast Sulawesi. Daily price data were collected from the Southeast Sulawesi Food Security Agency from January 2023 to December 2025. The LSTM model consisted of two LSTM layers (128 and 64 units), a Bahdanau Attention mechanism, and an Embedding layer to represent regions and commodities. The XGBoost component was used to correct LSTM residuals within a 30-day prediction horizon. Model performance was evaluated using Mean Absolute Percentage Error (MAPE). Results showed that the Hybrid LSTM-XGBoost model achieved better accuracy, with an average MAPE of 4.73% compared to 5.52% for the single LSTM model. Significant improvements were found in volatile commodities such as Bird’s Eye Chili and Curly Red Chili. The system can support production planning, food distribution, and SDG 1 (No Poverty).
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Perancangan Carousel Instagram sebagai Media Edukasi Fibonacci Retracement pada Trading Forex
The development of digital technology has encouraged the use of Instagram as a visual education medium, including for delivering forex trading material. One topic that remains difficult for beginner traders to understand is Fibonacci Retracement, especially in identifying swing high, swing low, entry areas, and price targets. This study aims to design an Instagram carousel as an educational medium for Fibonacci Retracement that is informative, visually appealing, and easy for beginner traders to understand. This research uses the Design Thinking method through five stages: Empathize, Define, Ideate, Prototype, and Test. Data were collected through literature study, observation of trading education content on Instagram, audience needs exploration, and questionnaires distributed to 8 respondents who matched the target users. The findings show that audiences need material that is concise, visual, sequential, and supported by trading chart examples. The carousel prototype received positive responses, as most respondents considered the design attractive, the colors clear, the font readable, the text size appropriate, and the material sequence easy to follow. However, several parts still need improvement, especially entry explanation, swing high, swing low, and target extension. This study implies that Instagram carousel can be an alternative visual education medium to help beginner traders understand Fibonacci Retracement more simply and systematically.
Perancangan Prototype Sistem Manajemen Quiz Berbasis Web Untuk Mendukung Evaluasi Pelatihan Menggunakan Figma
The development of information technology has provided convenience in supporting training activities and competency evaluation within companies. Training is one of the important activities carried out to improve participants’ knowledge, skills, and abilities in accordance with organizational needs. To determine the success of training, an evaluation process is required to measure participants’ level of understanding of the material that has been delivered. Based on observations conducted at a company, the training evaluation process still requires a more structured, interactive, and user-friendly medium. The company’s identity is not directly disclosed due to its confidentiality policy; therefore, the research object in this article is referred to as Company X. This study focuses on designing a prototype of a dashboard Quiz Management System using Figma. The system is designed to support question management, quiz library, quiz implementation through QR Code, quiz result presentation, question discussion, and leaderboard features. The purpose of this study is to produce a prototype design that can serve as an initial reference for developing a web-based training evaluation system that is more modern, structured, interactive, and suitable for user needs.
Perancangan UI/UX Prototype Sistem Informasi Pendataan Tamu di Sebuah Perusahaan Berbasis Website Menggunakan Metode Design Thinking
Guest data collection is an important administrative activity in supporting the management of visitor information in a company. However, the manual guest data collection process often causes various obstacles, such as recording errors, difficulty in searching for data, and ineffective reporting processes. This study aims to design the User Interface (UI) and User Experience (UX) prototype for a website-based Guest Data Collection Information System using the Design Thinking method. The Design Thinking method was chosen because it is oriented towards user needs through five stages: empathize, define, ideate, prototype, and test. Data collection was carried out through observation and identification of user needs for the guest data collection system. The results of the study are an interface prototype designed using Figma, consisting of a login page, dashboard, guest data collection, guest data, and reports. The resulting prototype is expected to provide an overview of a system that is easier to use, has clear navigation, and supports the guest data collection process more effectively. This research can be a reference in the development of a guest data collection information system in the next implementation stage.
Evaluation of WLAN Network Speed Based on Service Access Time and Proposed Expansion of Guest Network Infrastructure at BPJS Ketenagakerjaan Palembang Branch
This study evaluates the performance of the existing WLAN network on the first floor of the BPJS Ketenagakerjaan Palembang Branch office and proposes an expansion of guest network infrastructure on the second floor. Network performance was measured using Wireshark and Speedtest across three access-time scenarios (busy, moderate, and quiet hours), based on the TIPHON Quality of Service parameters: throughput, delay, jitter, and packet loss. Results show that the existing flat-network topology causes severe performance degradation during busy hours, with a QoS index of 2.00 (fair/critical), throughput dropping to 0.96 Mbps, delay rising to 338 ms, jitter spiking to 792.6 ms, and packet loss reaching 9.35%. Based on these findings, a second-floor guest network design is proposed using VLAN segmentation, a captive portal with a two-hour session limit, and per-user bandwidth throttling of 2 Mbps. The proposed design isolates public traffic from the internal network while maintaining fairness of access for visitors, offering a practical and secure solution for expanding wireless infrastructure in public-service institutions.
Pendidikan Matematika Inklusif untuk Siswa Tunarungu: Pengembangan Model YOLO Ringan untuk Deteksi Operator Aritmetika Dua Tangan
This study presents a real-time recognition system for two-handed arithmetic operators (Plus, Minus, Multiply, Divide) to support inclusive mathematics education for deaf students. Utilizing the YOLO26 architecture, the research evaluates four configurations—Nano and Small variants at 640px and 720px resolutions—trained on a newly developed private dataset of 2,828 images. Experimental results demonstrate exceptional accuracy, with all configurations achieving mAP50 scores above 0.99. The YOLO26-s variant at 720px provided the highest precision (0.9842), while benchmarking on a local GTX 1650 GPU confirmed real-time viability with frame rates reaching up to 88 FPS. Even when deployed on a mobile CPU (i5-12500H), the lightweight Nano models maintained a functional execution speed of 23 FPS. These findings confirm that optimized deep learning models can accurately interpret complex mathematical gestures on consumer-grade hardware. Ultimately, this lightweight framework successfully bypasses heavy skeletal tracking overhead, providing a highly robust and accessible foundation for interactive digital educational media.
Penerapan Model Antrean FIFO untuk Meningkatkan Efisiensi Pelayanan pada Bank Mini Syari’ah di Pondok Pesantren Syalafiyah Syafi’iyah Sukorejo
This research examines the operational dynamics of the First In First Out (FIFO) queuing approach at the Bank Mini Syari'ah unit of Pondok Pesantren Salafiyah Syafi'iyah Sukorejo, a pesantren-run financial service handling santri's day-to-day transactions. Long queues remain common in Islamic financial institutions, often persisting even after FIFO is formally adopted, leaving customer dissatisfaction largely unresolved. This research describes how FIFO is designed, applied, and interpreted by Customer Service staff and santri within the pesantren's distinct social setting. A descriptive qualitative method was used, drawing data from in-depth interviews, direct observation of daily queue behavior, and transaction documentation dated 20 June 2026. Data were processed through interactive analysis following Spradley and Miles-Huberman, with credibility secured via source and technique triangulation. Results show FIFO realized through a Java-based program separating Teller and Customer Service queues; field testing revealed transaction peaks clustered in mid-morning hours while officer workloads stayed fairly balanced. The study finds FIFO improves order over the earlier informal "first come first served" practice, though its success rests less on algorithmic precision and more on how fairness and discipline are embraced within pesantren culture. Practical suggestions include consistent numbering systems and stronger floor management during peak hours.
Rancang Bangun Sistem Informasi Penjualan dan Stok Air Galon Berbasis Website pada Usaha Depot Air Isi Ulang Cemani
This research aims to design and build a website-based information system for gallon water sales and stock at the Cemani Refill Water Depot. The current problem is that the sales and stock recording process is still done manually, resulting in frequent recording errors, delays in reporting, and difficulties in monitoring inventory. This condition results in less effective and efficient operational processes. The system development method used in this research is a prototype method, which includes requirements gathering, prototype creation, prototype evaluation, system development, system testing, system evaluation, and system use. The system design was carried out using Unified Modeling Language (UML), while system testing utilized Black Box Testing.The result of this research is a web-based information system capable of managing sales data, gallon water stock data, customer data, and generating reports quickly and accurately. Based on the results of testing using the Black Box Testing method, all system features, such as login, customer data management, stock management, sales transactions, and report generation, functioned as expected without error. This system is expected to improve work efficiency, minimize recording errors, and facilitate business owners in monitoring business progress in real time.
Perancangan Sistem Informasi Pendaftaran dan Pengelolaan Ekstrakulikuler di SMK Swasta Nahdlatul Ulama 2 Medan Menggunakan Metode Waterfall
The management of extracurricular activities at SMK Swasta Nahdlatul Ulama 2 Medan is still conducted manually, making the registration process, data management, and report generation less effective and more prone to administrative errors. This study aims to design and develop a web-based information system for extracurricular registration and management to improve the effectiveness and efficiency of administrative processes. The research employed an applied research approach using software engineering principles with the Waterfall development model, consisting of requirements analysis, system design, implementation, testing, and maintenance. Data were collected through observations and structured interviews with the Vice Principal for Student Affairs and extracurricular supervisors. The results indicate that the developed system simplifies the registration process and supports integrated management of student, supervisor, and extracurricular activity data. Furthermore, the system provides faster, more accurate, and easily accessible information, facilitating administrative tasks, data retrieval, report generation, and activity monitoring. Therefore, the web-based information system developed using the Waterfall method effectively improves the quality and efficiency of extracurricular activity management at the school.
Klasifikasi Tinggi Awan dari Citra Satelit Himawari-8 Menggunakan Fuzzy C-Means, GLCM, dan KNN.
Monitoring ketinggian awan merupakan parameter penting dalam observasi meteorologi yang berperan strategis dalam analisis dinamika atmosfer serta prediksi cuaca ekstrem. Penelitian ini bertujuan mengembangkan sistem klasifikasi otomatis untuk mengategorikan awan rendah, awan menengah, dan awan tinggi menggunakan citra satelit Himawari-8. Metodologi penelitian mencakup empat tahapan utama: pre-processing untuk standardisasi data, segmentasi menggunakan metode Fuzzy C-Means (FCM) untuk memisahkan objek awan, ekstraksi fitur tekstur berbasis Gray Level Co-occurrence Matrix (GLCM) dengan sembilan parameter, serta klasifikasi menggunakan algoritma K-Nearest Neighbor (K-NN). Data penelitian bersumber dari BMKG Stasiun Meteorologi Cengkareng, Tangerang. Pengujian sistem dilakukan melalui empat skenario eksperimen dengan volume data uji sebanyak 20, 30, 40, dan 50. Hasil penelitian menunjukkan bahwa sistem mampu mendeteksi kategori awan rendah dengan tingkat akurasi yang sangat tinggi, bahkan mencapai performa sempurna pada beberapa parameter efektivitas. Fenomena tumpang tindih (overlapping) pada nilai parameter fitur tekstur antar kelas menyebabkan algoritma K-NN sulit menentukan batas keputusan yang optimal. Secara keseluruhan dari 3 ketinggian awan yang dilakukan, awan rendah dapat diklasifikasikan dengan baik dibanding dengan awan tengah dan awan tinggi. Dari 4 percobaan hasil akurasi untuk awan rendah adalah 100%. Sedangkan untuk awan tengah akurasi yang dihasilan 72,45% dan awan tinggi 90,95%. Penelitian ini merekomendasikan adanya integrasi yang lebih adaptif antara tahap segmentasi dan ekstraksi ciri guna meminimalisir bias klasifikasi pada satu kelas tertentu dan meningkatkan akurasi sistem secara menyeluruh pada seluruh tingkatan awan.
Prediksi Kelulusan Mahasiswa Menggunakan Random Forest Dan Smote Untuk Mengatasi Imbalanced Data
Institusi pendidikan tinggi masih menghadapi tantangan dalam mengidentifikasi risiko keterlambatan kelulusan mahasiswa secara dini, terutama akibat kompleksitas data akademik dan kendala imbalanced data. Penelitian ini bertujuan untuk membangun model prediksi kelulusan mahasiswa yang akurat dengan mengombinasikan algoritma Random Forest dan teknik SMOTE guna menangani kesenjangan jumlah sampel antara kelas mayoritas dan minoritas. Metode yang digunakan adalah eksperimen kuantitatif berbasis Data Mining dengan memanfaatkan bahasa pemrograman Python di lingkungan Google Colab. Dataset yang terdiri dari 1.500 catatan akademik diproses melalui tahap pra-pemrosesan, penyeimbangan data menggunakan SMOTE, hingga klasifikasi menggunakan mekanisme ensemble learning pada Random Forest. Hasil eksperimen menunjukkan performa yang sangat memuaskan dengan tingkat akurasi mencapai 98,75% serta nilai precision, recall, dan f1-score yang stabil. Analisis kepentingan atribut mengungkapkan bahwa Nilai Tugas dan Aktivitas E-Learning merupakan indikator prediktor yang paling berpengaruh dalam menentukan status kelulusan. Model ini memiliki potensi besar untuk diimplementasikan sebagai sistem peringatan dini (Early Warning System) guna membantu institusi dalam memitigasi angka keterlambatan kelulusan secara otomatis dan efisien..
Rancang Bangun Sistem Pengelolaan Informasi Kerusakan Fasilitas Pada Dinas Pengairan Aceh Berbasis Web
Penelitian ini bertujuan untuk merancang dan membangun sistem pengelolaan informasi kerusakan fasilitas berbasis web pada Dinas Pengairan Aceh yang dilengkapi dengan fitur Laporan Darurat, sehingga masyarakat umum dapat melaporkan kerusakan infrastruktur pengairan secara langsung. Latar belakang penelitian ini didasari oleh belum tersedianya kanal pelaporan kerusakan pada website resmi Dinas Pengairan Aceh, sehingga penyampaian informasi kerusakan masih dilakukan secara konvensional melalui telepon atau kunjungan langsung ke kantor dinas, yang berdampak pada lambatnya proses penanganan. Metode penelitian yang digunakan adalah metode pengembangan perangkat lunak Waterfall, yang terdiri atas tahap analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan pemeliharaan, dengan pemodelan sistem menggunakan Unified Modeling Language berupa Use Case Diagram dan Activity Diagram. Sistem dibangun menggunakan bahasa pemrograman HTML, CSS, JavaScript, Node.js, dan basis data MySQL, kemudian diuji menggunakan metode Black Box Testing terhadap 30 skenario pengujian yang mencakup pengiriman laporan, unggah foto, penentuan lokasi melalui peta digital, pelacakan status laporan, verifikasi oleh admin, dan penugasan petugas lapangan. Hasil penelitian menunjukkan bahwa seluruh fungsi yang diuji berjalan sesuai dengan spesifikasi yang dirancang dan dinyatakan valid. Sistem yang dihasilkan mampu meningkatkan kecepatan, akurasi, dan transparansi proses pelaporan kerusakan infrastruktur pengairan, sekaligus memperkuat komunikasi dua arah antara masyarakat dan instansi pemerintah, sejalan dengan prinsip good governance dan semangat e-government yang partisipatif.
Pemanfaatan Chatbot Berbasis Perangkat Pintar dengan Pendekatan Interaktif-Partisipatif untuk Pendampingan Kader Kesehatan: Narrative Literature Review
Community health workers play a vital role in community-based healthcare but often face challenges related to limited training, access to reliable information, and continuous supervision. This study aimed to examine the use of smart device–based chatbots with an interactive and participatory approach to support community health workers. A Narrative Literature Review was conducted on 30 Indonesian- and English-language articles published between 2019 and 2026. The findings indicate that chatbots function as educational resources, consultation tools, case simulation media, monitoring systems, and supervision support. Smartphone-based platforms, particularly WhatsApp, enhance accessibility because they are widely used by community health workers. Interactive features, including two-way communication, quizzes, simulations, and immediate feedback, improve users’ knowledge, skills, confidence, and work practices. Participatory development involving community health workers and health professionals increases system relevance and usability. However, implementation remains constrained by digital literacy, internet access, data privacy, information validity, and unequal device availability. Chatbots should therefore be implemented as complementary technologies supported by professional supervision, validated knowledge resources, and clear referral mechanisms to strengthen community-based health services.
Integrasi Pipeline Big Data NoSQL dan Pemrosesan Bahasa Alami untuk Manajemen Kualitas Berbasis Voice of Customer
The exponential growth of customer review data on e-commerce platforms generates high volume, variety, and velocity of data, necessitating efficient Big Data Analytics approaches beyond the constraints of traditional relational databases. This study aims to analyze product review sentiments to identify customer satisfaction patterns and inconsistencies between star ratings and textual reviews, while evaluating the efficiency of NoSQL databases in managing such non-relational data structures. The method employed involves text mining and Natural Language Processing (NLP), applying data preprocessing, Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction with bigram-with-punctuation modeling, and text classification using the TextBlob library. The unstructured review data is managed using a NoSQL MongoDB database cluster with a scale-out architecture. The results indicate that integrating symbol-only word filtration significantly improves keyword relevance, and n-gram modeling successfully captures negation contexts like "not good". Word cloud visualizations effectively extract dominant keywords in negative reviews (Score 1), highlighting product quality criticisms ("taste", "product") and origin concerns ("made in"). Furthermore, sentiment distribution inconsistencies were detected, wherein low-rating reviews scores 1 and 2 still exhibited 15.1% and 29.2% positive sentiment, respectively. The implications of this research provide strategic recommendations for e-commerce executive management to integrate real-time sentiment monitoring systems for data-driven decision-making, proactive product development, and customer service optimization.
Perancangan dan Pengembangan Website Kajian Islam Terintegrasi dengan WhatsApp
Pengelolaan kegiatan kajian Islam selama ini masih banyak dilakukan dengan cara konvensional, sehingga penyebaran informasi menjadi terpecah-pecah, pendaftaran peserta tidak efisien, dan pengelolaan data administrasi menjadi sulit. Penelitian ini bertujuan untuk merancang dan mengembangkan Sistem Manajemen Kajian Islam berbasis web yang terintegrasi dengan WhatsApp guna meningkatkan penyebaran informasi, pendaftaran peserta, dan komunikasi antara panitia dan jamaah. Penelitian ini menggunakan pendekatan kuantitatif deskriptif dengan desain penelitian rekayasa sistem, sedangkan proses pengembangan perangkat lunak mengikuti model Waterfall, meliputi analisis kebutuhan, perancangan sistem, implementasi, pengujian, penerapan (deployment), dan pemeliharaan. Sistem dikembangkan menggunakan HTML, CSS, JavaScript, Node.js, REST API, dan SQLite. Sistem yang diusulkan menyediakan fitur seperti manajemen jadwal kajian, informasi penceramah, pendaftaran daring, pembuatan tiket digital, verifikasi peserta, pencatatan kehadiran, pengumuman, pelaporan, serta integrasi WhatsApp Click-to-Chat untuk konfirmasi pendaftaran. Evaluasi fungsional dilakukan menggunakan metode Black-box Testing untuk memverifikasi bahwa setiap fitur sistem berjalan sesuai dengan kebutuhan yang telah ditetapkan. Hasil pengujian menunjukkan bahwa seluruh fungsi yang diuji, termasuk manajemen jadwal, pendaftaran peserta, autentikasi, konfirmasi WhatsApp, pencatatan kehadiran, dan pelaporan, berjalan dengan baik tanpa kesalahan fungsional. Sistem yang dikembangkan menyediakan platform terpusat untuk mengelola kegiatan kajian Islam, meningkatkan efisiensi administrasi, mempermudah komunikasi dengan peserta, serta memperluas akses terhadap informasi kajian. Pengembangan lebih lanjut dapat mencakup integrasi dengan WhatsApp Business API resmi, absensi berbasis kode QR, serta evaluasi usability yang lebih komprehensif dengan melibatkan jumlah pengguna yang lebih banyak.
Rancang Bangun Sistem Inventory Gudang Logistik Berbasis Web Menggunakan Metode Prototype
Managing goods in a logistics warehouse requires a system capable of providing rapid, accurate, and integrated information. Manual recording processes can lead to inaccurate stock data, duplicate records, and delays in report generation. This study aims to develop a web-based logistics warehouse inventory information system for PT Sinar Suri (Kaimana Branch) using the Laravel framework and the Prototyping method. System development began with identifying requirements through interviews, followed by Prototype creation, user evaluation, implementation using Laravel and MySQL, and testing via Black Box Testing and the System Usability Scale (SUS). The resulting system features role-based authentication, facilitates the management of master item data, records inbound and outbound transactions, automatically monitors stock levels, and streamlines inventory report generation. This system enhances inventory management effectiveness by enabling organized control and monitoring of item information; this reduces recording errors, accelerates reporting, and improves stock control quality, ultimately supporting operational decision-making within the warehouse.
Stanisslaus Giovanka Vega Saputra Kristiawan
Strategi Membangun Citra Rumah Sakit Adhyaksa Jawa Timur melalui Media Sosial
In the digital era, social media has become an important communication strategy for organizations, including hospitals, in building and strengthening institutional image. Adhyaksa East Java Hospital utilizes Instagram and TikTok platforms as communication channels to disseminate health information, provide educational content, and establish relationships with the public. This study aims to describe and analyze social media communication strategies implemented through the accounts @rsadhyaksajatim and @rsadhyaksa.jatim in building the hospital’s image. This research employed a qualitative descriptive method, with data collected through observation, in-depth interviews, and documentation. The data were analyzed using the interactive model of Miles, Huberman, and Saldana, consisting of data reduction, data display, and conclusion drawing. The findings indicate that social media plays a strategic role in shaping the hospital image through informative, educational, and communicative content. The effectiveness of the strategy is supported by content consistency, attractive visual presentation, and active audience interaction. Engagement levels between the hospital and social media users serve as important factors in strengthening public trust and creating a positive perception of the institution. This study highlights the importance of optimizing digital communication strategies to support sustainable hospital branding in the digital era.
Perancangan Sistem Rekomendasi Produk Menggunakan Metode Decision Tree Berdasarkan Karakteristik Pelanggan Dan Riwayat Transaksi Studi Kasus PT Diamond Interest
Diamond Interest sebagai perusahaan distribusi produk herbal memiliki data karakteristik pelanggan dan riwayat transaksi yang berpotensi dimanfaatkan untuk mendukung strategi pemasaran berbasis data. Namun, data tersebut belum dioptimalkan untuk menghasilkan rekomendasi produk yang sesuai dengan kebutuhan pelanggan. Penelitian ini bertujuan merancang sistem rekomendasi produk menggunakan metode Decision Tree berdasarkan karakteristik pelanggan dan riwayat transaksi guna menghasilkan rekomendasi yang akurat dan relevan. Penelitian menggunakan pendekatan kuantitatif dengan kerangka kerja CRISP-DM yang meliputi Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, dan Deployment. Dataset yang digunakan berasal dari data pelanggan, transaksi, dan produk herbal PT Diamond Interest periode 17 Mei 2025 hingga 17 Mei 2026. Model Decision Tree dibandingkan dengan Random Forest dan Support Vector Machine (SVM), kemudian dievaluasi menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa Decision Tree memberikan kinerja terbaik dengan nilai accuracy sebesar 80,77%, precision 80,92%, recall 80,77%, dan F1-score 80,58%, serta berada pada kondisi good fit tanpa indikasi overfitting maupun underfitting. Model tersebut diimplementasikan ke dalam sistem informasi berbasis web menggunakan Streamlit yang menyediakan fitur unggah dataset, visualisasi data, klasifikasi preferensi kesehatan pelanggan, dan rekomendasi produk herbal. Sistem yang dihasilkan mampu memberikan rekomendasi produk yang lebih tepat sasaran sehingga mendukung pengambilan keputusan pemasaran berbasis data dan meningkatkan efektivitas strategi pemasaran perusahaan.
Sistem Informasi Inventarisasi Sarana dan Prasarana Sekolah Berbasis Website pada SMPN 292 Jakarta Barat
Inventory management of school facilities and infrastructure is an important activity in supporting effective and efficient asset administration. SMPN 292 West Jakarta still manages inventory data manually, resulting in difficulties in recording, searching, monitoring assets, and generating inventory reports. This study aims to design and develop a web-based School Facilities and Infrastructure Inventory Information System to support integrated asset management. The research employed the Research and Development (R&D) method consisting of problem identification, literature review, data collection, requirements analysis, system design, implementation, and system testing. The system was designed using Unified Modeling Language (UML) and Entity Relationship Diagram (ERD), while system testing was conducted using the Black Box Testing method. The developed system provides features for managing inventory data, storage locations, acquisition sources, user management, and inventory reporting. The results indicate that the developed information system improves the efficiency, accuracy, and effectiveness of inventory management compared to manual procedures. Furthermore, the Black Box Testing results show that all system functionalities operate according to user requirements. Therefore, the proposed system is considered suitable for supporting facilities and infrastructure management at SMPN 292 West Jakarta.
Perancangan dan Implementasi Aplikasi E-Commerce Berbasis Android pada UMKM Radeaa-Store
The development of information technology encourages MSMEs to utilize digital systems to improve operational effectiveness and customer servise. Radeaa Store as an MSMEs operating in the fashion selector still faces obstracles in managing products, transactions and preparing reports which are carried out conventionally. This research aims to design and implement an Androidbased e-commerce application to support the digitalization of business processes at Radea-Store. The system development method used is waterfall which consists of planning stages, need analysys, design, implementation and testing. The application was developed using Flutter for mobile, Laravel for the web admin dashboard, and Midtrans as a payment gateway. The research results show that the application is successful in providing product, order, customer, sales, report, and online payment management features. Based on black box testing, all main features work according to system requirements. This application is able to increase operational efficiency and provide easy transactions for customers and MSME managers.
Implementasi Infrastruktur TI Berbasis Virtualisasi untuk Meningkatkan Efisiensi dan Fleksibilitas Sistem Informasi di SMK Modis Tenjo
The development of information technology encourages educational institutions to have IT infrastructure that is efficient, flexible, and capable of optimally supporting information system services. However, the use of separate physical servers often results in low resource utilization, high operational costs, and limitations in system management. This study aims to implement virtualization-based IT infrastructure to improve the efficiency and flexibility of the information system at SMK MODIS Tenjo. The research method used is an experiment with a quantitative approach through a pre-test and post-test design. The research stages include needs analysis, virtualization system design, VMware ESXi implementation, and migration of physical servers to virtual (Physical to Virtual/P2V), as well as testing and evaluating system performance. The research results show that the implementation of virtualisation successfully consolidated 15 physical servers into 3 host servers with several virtual machines without reducing service quality. In addition, CPU utilisation increased from 12% to 75%, operational costs decreased by up to 46%, and server deployment time was reduced from 5 days to less than 2 hours. The implementation of High Availability (HA) features also improved system reliability by reducing service recovery time from 3 days to less than 2 hours. Based on these results, virtualisation has proven effective in enhancing the efficiency, flexibility, and reliability of IT infrastructure in the educational environment.
Rancang Bangun Aplikasi Desktop Pendeteksi Duplikat Data NIK dan NKK pada File Excel Menggunakan Metode Exact Matching Berbasis Flutter
The management of UMKM assistance recipient data at the Cooperatives and Micro Enterprises Office of Kediri Regency is still carried out manually using Microsoft Excel, resulting in frequent data duplication in the NIK and NKK columns. Excel's limitation of accurately processing only up to 15 digits is a serious obstacle because NIK consists of 16 digits, risking digit truncation that causes data inaccuracy and inaccurate aid distribution. This study aims to design and build a Flutter-based desktop application capable of detecting duplicate NIK or NKK data automatically, accurately, and informatively. The method used is Research and Development (R&D) with the Sugiyono development model simplified into eight stages, along with the Exact Matching algorithm based on HashMap which has O(n) time complexity. System testing was conducted using the Black Box Testing method through ten test scenarios covering file validation, NIK/NKK duplicate detection, result export, and application reset. The results showed that the application successfully detected all duplicate data with 100% accuracy and was able to process Excel files containing 5,000 rows of data in less than 1 second, much faster than the manual method which takes 2 to 3 hours. All ten Black Box Testing scenarios were declared successful (100% pass rate). The implication of this research is the availability of an appropriate technical solution that can be directly used by staff to accelerate data validation, and can serve as a reference for developing similar systems in other government institutions facing similar data duplication problems.
Digitalisasi Proses Pengajuan Pinjaman Berbasis Web Menggunakan Metode Agile pada PT. Mekar Harum Dinamika
PT. Mekar Harum Dinamika (PT. MHD) is a company that provides fronting services for loan applications submitted by Indonesian Civil Servants, namely the State Civil Apparatus (ASN) or Civil Servants (PNS). Customer data management and loan application processing are still carried out using Microsoft Excel, while document exchange is conducted through WhatsApp. These practices result in a high risk of recording errors, data duplication, difficulties in retrieving information, and delays in report generation, which reduce the overall effectiveness of business processes. This study aims to implement a web-based loan application management information system to support customer data management, loan application processing, incentive submission, document verification, application status monitoring, and integrated report generation. The system was developed using the Agile methodology, enabling iterative development and continuous adaptation to user requirements. The application was built using the Laravel framework and MySQL database to ensure structured and secure data management. The results of this study indicate that the developed system improves the effectiveness and efficiency of loan application management, reduces data processing errors, accelerates information retrieval and report generation, and enables administrators and marketing staff to monitor the entire loan application process in real time. Consequently, the system supports faster and more accurate decision-making while enhancing the overall quality of loan management services.
Smart Traffic Light Berbasis ESP32 dan Internet of Things menggunakan Logika Fuzzy untuk Pengendalian Kepadatan Lalu Lintas
Traffic congestion and zebra crossing violations are common problems at road intersections due to fixed-time traffic light systems that are less adaptive to traffic conditions. This research aims to design and implement an ESP32 and Internet of Things (IoT)-based Smart Traffic Light system using the Mamdani Fuzzy Logic method to automatically regulate traffic light duration based on vehicle density. The system utilizes an inframerah sensor to detect vehicle density and an ultrasonic sensor to detect zebra crossing violations. Sensor data are processed by the ESP32 through fuzzification, inference, and defuzzification to determine the appropriate traffic light duration. The system is equipped with traffic light LEDs, a TM1637 countdown timer, a buzzer, and a Telegram Bot for real-time monitoring. Testing results showed that the inframerah sensor successfully detected vehicle density, while the ultrasonic sensor accurately detected objects at distances of 5–25 cm. The fuzzy logic system produced green light durations of 5, 20, and 30 seconds for low, medium, and high traffic densities, respectively. In addition, the Telegram Bot successfully sent real-time notifications of zebra crossing violations. The proposed system operates automatically, adaptively, and effectively to support traffic management and zebra crossing monitoring.
Implementasi Sensor VOC Digital pada Sistem Monitoring dan Pengendalian Asap Rokok dalam Ruangan Berbasis Internet of Things
Cigarette smoke is one of the sources of indoor air pollution that contains various harmful compounds, including Volatile Organic Compounds (VOC), which can reduce indoor air quality and negatively affect human health. This study aims to design and develop an indoor cigarette smoke control system based on the MiCS-5524 VOC sensor and Internet of Things (IoT) technology with monitoring through Telegram on an Android device. The proposed system uses an Arduino Uno as the main controller integrated with an ESP8266 module, MiCS-5524 VOC sensor, L298N motor driver, DC fan, activated carbon filter, buzzer, and 16×2 I2C LCD. The test results show that the MiCS-5524 sensor is capable of detecting changes in VOC concentration caused by cigarette smoke exposure, while the system automatically activates the fan and buzzer according to the detected air quality conditions. The ESP8266 module successfully transmits monitoring data to Telegram Android in real-time with an average transmission time of 2.3 seconds. Furthermore, the activated carbon filter contributes to reducing VOC levels, resulting in improved indoor air quality. The results indicate that the developed system is capable of performing automatic detection, control, and monitoring of indoor air quality.
Graph Neural Networks for Financial Fraud Detection
Financial fraud is becoming increasingly complex alongside the growth of digital financial ecosystems, causing conventional fraud detection approaches based on handcrafted rules and tabular machine learning models to face limitations in identifying coordinated fraudulent activities. This study aims to analyze the application of Graph Neural Networks (GNNs) for financial fraud detection by utilizing transaction attributes and graph structural information. A quantitative experimental approach was conducted using the publicly available Elliptic Bitcoin Transaction Dataset, where transactions were modeled as graph nodes and relationships between transactions as graph edges. The proposed method implemented a Graph Attention Network (GAT) and compared its performance with Graph Convolutional Networks (GCN), GraphSAGE, and Graph Isomorphism Networks (GIN). Data preprocessing involved feature normalization, graph construction, and supervised learning on labeled transaction data. Model evaluation was performed using Accuracy, Precision, Recall, F1-score, ROC-AUC, and PR-AUC metrics. The results show that attention-based graph learning provides superior performance in detecting fraudulent transactions by assigning adaptive importance to relevant neighboring nodes during information propagation. Furthermore, graph representation learning effectively captures interconnected fraud patterns that are difficult to identify using conventional machine learning methods. These findings highlight the potential of GNNs as an intelligent approach for improving financial fraud detection and supporting risk management systems in increasingly complex digital financial environments.
Klasifikasi Keluhan Pengguna Berbasis Large Language Model untuk Layanan Digital
The rapid growth of digital services such as e-commerce applications, digital banking, and app-based public services has significantly increased the volume of user complaints, making manual complaint classification inefficient. This study aims to develop and evaluate a Large Language Model (LLM)-based approach for classifying user complaints on digital services using a zero-shot prompting scheme. A small sample of 60 user complaints was collected and grouped into five categories: customer service, application or technical issues, payment and transaction, delivery, and data security and privacy. The research method consists of data collection, text preprocessing, labelling, LLM-based classification, and model performance evaluation. Testing on the small sample shows that the LLM was able to classify complaints with an accuracy of 86.7%, a weighted precision of 87.2%, a weighted recall of 86.7%, and a weighted F1-score of 86.8%. Most misclassifications occurred between contextually overlapping categories, namely payment or transaction and application or technical issues. The findings indicate that LLMs have strong potential as an efficient and accurate solution to support automated user complaint handling in digital services.
Analisis Sentimen Ulasan Aplikasi Duolingo di Google Play Store Menggunakan Algoritma Bidirectional Long Short-Term Memory (Bi-LSTM)
Reviews posted by users on the Google Play Store provide valuable feedback that can be utilized to measure user satisfaction and assess the quality of mobile applications. However, the growing volume of reviews makes manual evaluation increasingly impractical, highlighting the need for automated sentiment analysis techniques. This research proposes the use of the Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm to classify the sentiment of Indonesian-language reviews for the Duolingo application. The review dataset was obtained through web scraping from the Google Play Store and underwent several preprocessing steps, including case folding, text cleaning, word normalization, tokenization, stopword removal, and stemming. After preprocessing, the data were divided into 80% training data and 20% testing data for model development and performance evaluation. The effectiveness of the model was measured using accuracy, precision, recall, and F1-score. The experimental results yielded an accuracy of 94.05%, precision of 88.46%, recall of 92.15%, and an F1-score of 91.17%. These findings indicate that the Bi-LSTM model is capable of capturing sentiment patterns with a high level of reliability, although its ability to classify negative reviews is still influenced by the imbalance between sentiment classes. Overall, the study confirms that Bi-LSTM is a suitable deep learning approach for sentiment classification of application reviews and offers meaningful insights that can support Duolingo developers in evaluating user opinions and enhancing application quality.
Implementasi Sistem Pendukung Keputusan Pemilihan Produk Makanan Ringan Unggulan Kalimantan Selatan Menggunakan Metode Topsis Berbasis Web
Pemilihan produk makanan ringan unggulan khas Kalimantan Selatan merupakan proses yang memerlukan evaluasi multi-kriteria untuk mengidentifikasi produk terbaik yang layak mendapat perhatian, pembinaan, dan promosi dari pemerintah daerah. Proses seleksi yang masih dilakukan secara manual dan subjektif sering menghasilkan keputusan yang tidak konsisten dan kurang transparan. Penelitian ini mengusulkan implementasi Sistem Pendukung Keputusan (SPK) menggunakan metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) berbasis web untuk menentukan produk makanan ringan unggulan Kalimantan Selatan. Metode TOPSIS dipilih karena kemampuannya menangani masalah Multi-Attribute Decision Making (MADM) secara efektif dengan mempertimbangkan jarak terhadap solusi ideal positif dan negatif secara bersamaan. Sistem dikembangkan menggunakan PHP 8 dan MySQL dengan fitur manajemen kriteria, sub kriteria, alternatif produk, penilaian, perhitungan TOPSIS enam langkah, serta manajemen pengguna dua level (Admin dan User). Kriteria yang digunakan meliputi daya tahan produk, ketersediaan bahan baku lokal, kapasitas produksi harian, dan biaya pengemasan. Hasil perhitungan menunjukkan Keripik Pisang Mahuli (A4) terpilih sebagai produk unggulan peringkat pertama dengan nilai preferensi tertinggi 0.7222, diikuti Amplang Ikan Kotabaru (A1) dengan nilai 0.7087. Sistem ini memberikan solusi praktis untuk otomatisasi proses pemilihan produk unggulan UMKM dengan tingkat transparansi perhitungan yang dapat dipertanggungjawabkan secara ilmiah.
Analisis Sentimen Pengguna Twitter terhadap Tren Cryptocurrency di Indonesia dengan Metode Support Vector Machine
The rapid growth of cryptocurrency as a digital asset has attracted increasing public attention in Indonesia, influened by the extensive adoption of the internet and social media. Among various social media platforms, Twitter (X) has become one of the primary platforms where users express opinions and engage in discussions about cryptocurrency. The information generated from these interactions can be utilized to identify public sentiment trends. This investigation intends to assess public opinion regarding cryptocurrency trends in Indonesia by applying the Support Vector Machine (SVM) algorithm. The research adopts the Knowledge Discovery in Database (KDD) methodology, which consists of data selection, preprocessing, transformation, data mining, and evaluation stages. Data were collected through a web crawling process using the Tweet Harvest application, resulting in 7,000 Indonesian-language tweets. After duplicate removal and preprocessing, 4,502 tweets were retained as the research dataset. Feature extraction was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method to convert textual data into numerical representations, while sentiment classification was conducted using a linear-kernel Support Vector Machine. Model performance was evaluated using a Confusion Matrix. The experimental results demonstrated that the proposed model achieved an accuracy of 82.35%, precision of 84%, recall of 83%, and an F1-score of 84%. These findings indicate that the combination of TF-IDF and Support Vector Machine provides effective performance for classifying public sentiment regarding cryptocurrency trends in Indonesia.
Perancangan Sistem Monitoring Pelanggaran Siswa Berbasis Android di SMKN 4 Kota Serang
This study aims to develop and evaluate an Android-based application for monitoring student violations at SMK Negeri 4 Kota Serang, which previously relied on a manual, ledger-based recording system that was slow, error-prone, and difficult to track; interviews at the school found that more than 60% of 1,414 registered students had been recorded for a violation over three years. Using the Research and Development (R&D) method with a Waterfall model, the system was built with Kodular and Firebase Realtime Database and evaluated through black-box testing, media-expert validation, and user (counselor) response testing. The application provides administrator and teacher roles with features for managing student, teacher, and class data, recording violations, generating class recaps, visualizing violation charts, and ranking violators. Black-box testing showed that all tested functions (100%) worked as expected, expert validation produced an average score of 4.36 ("Good"), and user response testing produced an average score of 4.36 ("Very Good"). These findings indicate that the developed system is feasible for speeding up data recording, reducing manual recording errors, and supporting more structured student guidance.
Pengembangan Media Pembelajaran Interaktif Berbasis Web untuk Mendukung Pembelajaran TIK di SMA Santo Josef Freinademetz
Conventional ICT instruction at SMA Santo Josef Freinademetz often leads to passive learning and low conceptual comprehension among students. This study aims to develop and evaluate a valid, practical, and effective web-based interactive learning media utilizing the PHP Laravel 13 framework. A Research and Development (R&D) approach was adopted by integrating the ADDIE model with Prototyping methodology. Data were gathered through expert evaluation sheets, Likert-scale user response questionnaires, and learning achievement tests. The results indicate that the developed platform is highly valid based on expert reviews and exceptionally practical according to teacher and student feedback. Significantly, system implementation boosted students' academic performance in ICT and transformed the classroom atmosphere into a student-centered environment. This study implies that the web platform provides an impactful digital instructional solution to enhance learning efficiency and encourage student autonomy.