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Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Banjarbaru
ISSN : 20893787     EISSN : 26850893     DOI : -
Core Subject : Science,
Computer Science & IT
Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi adalah Jurnal Ilmiah bidang Teknik Informatika dan Sistem Informasi yang diterbitkan secara periodik tiga nomor dalam satu tahun, yaitu pada bulan April, Agustus dan Desember. Redaksi Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi menerima sumbangan tulisan hasil penelitian atau atau artikel konseptual bidang Teknik Informatika dan Sistem Informasi.
Arjuna Subject : -
Articles 1,007 Documents
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Model Aplikasi Layanan Informasi Pengaduan Masyarakat Di Kecamatan rosmawanti, nidia; Muslihuddin, Muslihuddin; Kirana, Eka Chandra
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 1 (2026): Februari 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i1.3479

Abstract

Public complaint services are an important component in efforts to improve the quality of public services within the local government environment. However, in many sub-districts, the complaint mechanism is still carried out manually or conventionally, resulting in various problems, such as delays in handling complaints, poorly organized report documentation, and low levels of information transparency to the public. This study aims to analyze system needs and design a model for public complaint information services based on information technology that can be implemented at the sub-district level. The research method used is system needs analysis through observation and interviews with sub-district officials, as well as literature studies related to the application of information systems in public services. The results of the study produced a model for a public complaint information system that is able to facilitate structured complaint recording, accelerate the follow-up process, and provide a feature for monitoring the status of complaints in real-time by the public. The implementation of the system model can improve the effectiveness, efficiency, and accountability of complaint services in sub-districts, as well as encourage the realization of more transparent and responsive public services.Keywords: Information Systems; Public Complaints; Public Services; Sub-districts; Information TechnologyAbstrakPelayanan pengaduan masyarakat merupakan salah satu komponen penting dalam upaya peningkatan kualitas pelayanan publik di lingkungan pemerintahan daerah. Namun, pada banyak kecamatan, mekanisme pengaduan masih dilakukan secara manual atau konvensional, sehingga menimbulkan berbagai permasalahan, seperti keterlambatan penanganan aduan, dokumentasi laporan yang tidak tertata dengan baik, serta rendahnya tingkat transparansi informasi kepada masyarakat.Penelitian ini bertujuan untuk menganalisis kebutuhan sistem dan merancang model layanan informasi pengaduan masyarakat berbasis teknologi informasi yang dapat diterapkan di tingkat kecamatan. Metode penelitian yang digunakan adalah analisis kebutuhan sistem melalui observasi dan wawancara dengan pihak kecamatan, serta studi literatur terkait penerapan sistem informasi dalam pelayanan publik.Hasil penelitian menghasilkan sebuah model sistem informasi pengaduan masyarakat yang mampu memfasilitasi pencatatan aduan secara terstruktur, mempercepat proses tindak lanjut, serta menyediakan fitur pemantauan status pengaduan secara real-time oleh masyarakat. Implementasi model sistem dapat meningkatkan efektivitas, efisiensi, dan akuntabilitas pelayanan pengaduan di kecamatan, serta mendorong terwujudnya pelayanan publik yang lebih transparan dan responsif.Kata Kunci: Sistem Informasi; Pengaduan Masyarakat; Pelayanan Publik; Kecamatan; Teknologi Informasi
Optimalisasi Layanan Informasi Mahasiswa Melalui Chatbot di Fakultas Ekonomika dan Bisnis UKSW Prameswari, Reskatirini Yastika; Wahyono, Teguh
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 1 (2026): Februari 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i1.3452

Abstract

The advancement of digital technology requires higher education institutions to provide academic information services that are fast, accurate, and easily accessible to students. The Faculty of Economics and Business at Satya Wacana Christian University (FEB UKSW) still faces limitations in providing responsive, always-available information services. This study aims to develop and evaluate a chatbot system as an efficient solution for academic information services. The research method employed is the Software Development Life Cycle (SDLC) using the Waterfall model, which consists of the stages of requirements analysis, system design, implementation, testing, and evaluation. The chatbot system was developed using Node.js for the backend and HTML, CSS, and JavaScript for the frontend. The dataset was constructed based on the following categories: academic services, financial information, promotion and cooperation, data and information center, and IT services. The testing results indicate that the chatbot achieves an average response time of 0.002 seconds and an accuracy rate of 95% for questions that are similar to those in the dataset. This study concludes that the developed chatbot effectively improves the speed and efficiency of academic information services, although further development is required to enhance its understanding of user queries.Kata kunci: Chatbot; Academic Information Services; Artificial Intelligence; Node.js; Natural Language Processing.AbstrakKemajuan teknologi digital menuntut perguruan tinggi untuk menyediakan layanan informasi akademik yang cepat, akurat, dan mudah diakses oleh mahasiswa. FEB UKSW masih menghadapi keterbatasan media layanan informasi yang responsif dan tersedia sepanjang waktu. Penelitian ini bertujuan untuk mengembangkan serta mengevaluasi sistem chatbot sebagai solusi layanan informasi akademik yang efisien. Metode penelitian yang digunakan adalah Software Development Life Cycle (SDLC) dengan model Waterfall, yang meliputi tahapan analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan evaluasi. Sistem chatbot dikembangkan menggunakan Node.js sebagai backend serta HTML, CSS, dan JavaScript pada sisi frontend. Dataset disusun berdasarkan kategori layanan akademik, keuangan, promosi dan kerja sama, pusat data dan informasi, serta layanan IT. Hasil pengujian menunjukkan chatbot memiliki waktu respons rata-rata 0,002 detik dan tingkat akurasi sebesar 95% untuk pertanyaan yang memiliki kemiripan dengan dataset. Penelitian ini menyimpulkan bahwa chatbot efektif meningkatkan kecepatan dan efisiensi layanan informasi akademik, meskipun masih perlu pengembangan pada pemahaman pertanyaan. 
Pengembangan Fitur Lanjutan Pada Aplikasi Manajemen Personal Individual Berbasis Mobile Android Mahulae, Billy Prestone; Widayati, Yohana Tri; Prakoso, Satrio Agung
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3168

Abstract

The rapid development of technology and personal management challenges among Indonesian youth require an integrated solution to manage both finances and time. This study develops advanced features on the Individual Personal Management Application (AMPI) for Android using the waterfall method, clean MVVM architecture, Coin dependency injection, and Firebase integration for authentication and data storage. A user needs analysis through an online questionnaire with 50 respondents showed a high interest in an integrated application with reminders, data visualization, task prioritization, and secure data storage. The system is designed using use case, activity, sequence, and class diagrams to support account, financial, and time management. Black-box testing confirmed that all features function as designed. The results demonstrate that AMPI effectively assists users, especially young people, in tracking daily transactions, managing schedules, and monitoring productivity and financial conditions.Keywords: Personal Management Application; Android; Financial Management; Time Management; FirebaseAbstrakPerkembangan teknologi dan tantangan manajemen personal pada generasi muda Indonesia menuntut solusi aplikasi yang dapat mengelola keuangan dan waktu secara terintegrasi. Penelitian ini mengembangkan fitur lanjutan pada Aplikasi Manajemen Personal Individual (AMPI) berbasis Android menggunakan metode waterfall, arsitektur MVVM clean, Koin dependen injeksi, serta integrasi Firebase untuk autentikasi dan penyimpanan data. Analisis kebutuhan pengguna melalui kuesioner daring pada 50 responden menunjukkan minat tinggi terhadap aplikasi ter-integrasi dengan fitur mengingat, visualisasi data, prioritas tugas, dan penyimpanan data aman. Sistem dirancang menggunakan use case, aktivitas, sequence, dan class diagram untuk mendukung manajemen akun, keuangan, dan waktu. Pengujian black-box menunjukkan seluruh fitur berfungsi sesuai rancangan. Hasil penelitian menunjukkan AMPI efektif membantu pengguna, khususnya generasi muda, dalam mencatat transaksi harian, mengatur jadwal, serta memantau produktivitas dan kondisi keuangan. 
Design of Diabetes Prediction Interface Using E-ss and Classification Tree Algorithm Venecia, Venecia; Hoendarto, Genrawan; Darmanto, Tony
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3370

Abstract

Diabetes was a chronic disease that continued to increase globally, making early detection essential to reduce long-term complications. This study aimed to develop a desktop-based diabetes prediction system that provided fast and simple classification results for medical personnel and individual users. The system used the entropy-based subset selection (E-ss) method to choose the most relevant attributes and a classification tree to classify the risk. The dataset from the National Institute of Diabetes and Digestive and Kidney Diseases, contained 768 patient records with attributes such as number of pregnancies, glucose level, blood pressure, and other risk factors. The E-ss process produced three attributes with the highest information scores, namely body mass index (BMI), blood pressure, and triceps skinfold thickness. These three attributes were then used as input to the classification tree model to generate diabetes risk predictions. Cross-validation testing showed an accuracy of up to 78.95%. These findings indicated that E-ss feature reduction helped maintain prediction performance while improving computational efficiency. This system was expected to serve as a practical and reliable diagnostic tool. 
Model Deteksi Serangan Jaringan Menggunakan Machine Learning Dengan Teknik Ensemble Learning Lauw, christopher Michael; Anggrawan, Anthony; Sulistianingsih, Neny
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 1 (2026): Februari 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i1.3369

Abstract

Network attacks pose a serious threat to the security of digital infrastructure, making accurate and efficient detection an urgent necessity. The National Cyber and Crypto Agency (BSSN) recorded 330,527,636 instances of anomalous traffic or network attacks. This sheer volume renders  manual  handling  by  cybersecurity  personnel  highly  impractical.  Consequently, a detection system based on Machine Learning (ML) is required. However, single machin e learning models have proven suboptimal in effectively resolving this complex problem. This research aims to develop a robust Network Intrusion Detection Model by employing advanced Ensemble Learning techniques—specifically Bagging, Boosting, and Stacking—to significantly optimize the performance of the base ML models. The study utilizes the UNR-IDD dataset. The methodology begins with comprehensive Data Preprocessing, including Min-Max Scaling and Dimensionality Reduction. Model performance is comprehensively evaluated using classification metrics across three distinct data splitting scenarios (70:30, 80:20, and 90:10) to identif y the optimal configuration. The experimental results demonstrate that the Stacking Ensemble Learning approach achieves the most optimal accuracy among the tested methods.Keywords: Network Intrusion Detection; Machine Learning; Ensemble Learning; Stacking; Dimensionality ReductionAbstrakSerangan jaringan telah menjadi ancaman serius bagi keamanan inf rastruktur digital . Badan Siber dan Sandi Negara (BSSN) mencatat terdapat 330.527.636 kasus traff ic anomali atau serangan jaringan. Hal tersebut sangat tidak memungkinkan personal keamanan siber untuk menanganinya. Maka, dibutuhkan sistem deteksi berbasis Machine Learning . model Machine Learning tunggal belum optimal dalam deteksi serangan jaringan. Penelitian ini bertujuan untuk memodelkan deteksi serangan jaringan menggunakan  Machine Learning dengan teknik ensemble learning bagging, boosting, dan stacking, guna untuk mengoptimalkan perf orma model Machine Learning. Penelitian ini menggunakan dataset UNR-IDD. Penelitian ini bertujuan mengembangkan Model  Deteksi  Serangan Jaringan yang robust menggunakan  teknik Ensemble Learning. Proses dimulai dengan Data Preprocessing, meliputi Min-Max Scaling dan Reduksi Dimensi. Model dievaluasi komprehensif menggunakan metrik klasif ikasi. Pada tiga skenario pembagian data (70:30, 80:20, 90:10) untuk mengidentif ikasi konf igurasi optimal. Hasil dari penelitian ini menunjukan bahwa Ensemble Learning stacking memilii akurasi yang paling optimal, dengan keseimbangan akurasi, presisi , F1 Score, dan Recall mencapai 100% . 
Data Warehouse for Study Program Accreditation Data Management at Private University X Putri, Tifani Anasya; Beng, Jap Tji; Trisnawarman, Dedi; Tiatri, Sri; Nagm, Fouad; Naidas, Michael S.; Salsabila, Tasya Mulia; Nurkholiza, Rahmiyana
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3157

Abstract

Technological advancements in the Fourth Industrial Revolution have transformed higher education, yet accreditation processes often suffer from inefficient data management, causing delays, inconsistencies, and limited monitoring of performance indicators. This study designs a data warehouse integrated with a dashboard and Early Warning System (EWS) to improve accreditation data management at University X, West Jakarta. Using Kimball’s Dimensional Model, data were structured into fact and dimension tables, with collection methods including interviews with the Head of the IT Department and document analysis of accreditation processes. Descriptive statistics were applied to identify trends and patterns. The dashboard enables real-time visualisation of key metrics, while the EWS issues alerts on missing or outdated data. Results show that the snowflake schema enhances data organisation and clarity, reducing manual processing and facilitating proactive monitoring. The system supports more efficient accreditation management and strengthens institutional readiness for assessment. 
Perbandingan Algoritma Machine Learning untuk Prediksi Potensi Tsunami di Pesisir Barat Lampung Stifan, Kevin Ramses; Fauzi, Chairani
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3184

Abstract

Indonesia has a high level of vulnerability to tsunami disasters due to seismic activity in subduction zones. This condition requires a fast and accurate tsunami potential prediction system to support effective disaster mitigation. This study aims to compare the performance of five machine learning algorithms in predicting tsunami potential along the West Coast of Lampung, namely Random Forest, K-Nearest Neighbor, Naïve Bayes, Neural Network, and Decision Tree. The dataset consists of 352 earthquake records from 2023–2024 obtained from BMKG, using parameters such as magnitude, depth, and epicentral distance. Model evaluation was conducted using a Confusion Matrix with performance metrics including accuracy, precision, recall, and F1-score. The results indicate that the Random Forest algorithm achieved the best performance with an accuracy of 100% and balanced precision, recall, and F1-score values. These findings are expected to support the development of a machine learning-based tsunami early warning system that is adaptive to local geophysical characteristics.Keywords: Earthquake; Machine Learning; Random Forest; Tsunami prediction AbstrakIndonesia memiliki tingkat kerentanan yang tinggi terhadap bencana tsunami akibat aktivitas gempa bumi di zona subduksi. Kondisi ini menuntut tersedianya sistem prediksi potensi tsunami yang cepat dan akurat untuk mendukung mitigasi bencana. Penelitian ini bertujuan membandingkan kinerja lima algoritma machine learning dalam memprediksi potensi tsunami di Pesisir Barat Lampung, yaitu Random Forest, K-Nearest Neighbor, Naïve Bayes, Neural Network, dan Decision Tree. Data yang digunakan berupa 352 catatan gempa bumi periode 2023–2024 yang bersumber dari BMKG dengan parameter magnitudo, kedalaman, dan jarak episentrum. Evaluasi model dilakukan menggunakan Confusion Matrix dengan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Random Forest memberikan performa terbaik dengan tingkat akurasi 100% serta keseimbangan nilai presisi, recall, dan F1-score. Temuan ini diharapkan dapat mendukung pengembangan sistem peringatan dini tsunami berbasis machine learning yang adaptif terhadap karakteristik geofisika lokal. 
Pengembangan Sistem Prediksi Harga Saham Berbasis Web Menggunakan Model LSTM dan CNN–LSTM Haeruddin, Haeruddin; Rinaldo, Rinaldo; Gautama, Gautama
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 1 (2026): Februari 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i1.3518

Abstract

This study discussed the challenges of stock price prediction, which is volatile and exhibits non-linear patterns, as well as the need for an easily accessible system to present prediction results quickly. A web-based application was developed to predict the closing prices of Indonesian non-banking stocks (ASII, TLKM, and UNVR) by utilizing daily market data that were updated periodically through an application programming interface. The integrated pre-trained models used were long short-term memory (LSTM) and convolutional neural network–long short-term memory (CNN-LSTM), which were integrated into the application for one-step-ahead (t+1) inference. The system development methodology followed the software development life cycle waterfall model. Functional testing using a black-box testing approach showed that the core features ran according to the requirements, so the application was considered suitable as a web-based medium for prediction and visualization.Keywords: Stock price prediction; Web application; Deep learning; Application programming interface; Non-banking stocksAbstrakPergerakan harga saham yang volatil dan non-linear menuntut pendekatan prediksi yang adaptif serta sistem yang mudah diakses. Penelitian ini bertujuan untuk mengembangkan suatu sistem prediksi harga penutupan saham dan menyajikannya secara cepat. Penelitian ini telah mengembangkan aplikasi berbasis web untuk prediksi harga penutupan saham emiten non-perbankan Indonesia (ASII, TLKM, dan UNVR) dengan memanfaatkan data pasar harian yang diperbarui berkala melalui application programming interface. Integrasi model terlatih yang digunakan yaitu long short-term memory (LSTM) dan convolutional neural network-long short-term memory (CNN-LSTM), yang diintegrasikan ke dalam aplikasi untuk proses inference satu langkah ke depan (t+1). Metodologi pengembangan sistem mengikuti software development life cycle model Waterfall. Sistem yang dikembangkan telah berfungsi sesuai dengan kebutuhan berdasarkan hasil black-box testing. 
Klasifikasi Genre Musik Menggunakan CNN Dengan Arsitektur Resnet-50 Dan Gradient Boost LightGBM Alessandro, Roberto; Tinaliah, Tinaliah
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 1 (2026): Februari 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i1.3458

Abstract

The rapid growth of digital music has driven the need for accurate and efficient automated music genre classification systems. This study evaluates a hybrid approach that integrates the ResNet-50 architecture as a feature extractor through transfer learning and LightGBM as a classifier. Using the GTZAN dataset represented as Mel-spectrograms, the research compares the effectiveness of hyperparameter optimization using Random Search and Grid Search methods. Based on performance evaluation, the hybrid scenario optimized with Grid Search yielded the best performance with an accuracy of 81.20%, outperforming the Random Search method. Nevertheless, the overall experimental results reveal that the end-to-end ResNet-50 model still provides superior performance compared to the hybrid approach. This indicates that the deep features from ResNet-50 are highly representative for separating genre classes, such that the addition of an external ensemble classifier does not yield significant improvements, although the hybrid approach still offers valuable empirical insights as a stable alternative model.Keywords: Convolutional Neural Network; ResNet-50; LightGBM; Mel-Spectogram; Klasifikasi Genre Musik;AbstrakPertumbuhan pesat musik digital mendorong kebutuhan akan sistem klasifikasi genre musik otomatis yang akurat dan efisien. Penelitian ini mengevaluasi pendekatan hibrida yang mengintegrasikan arsitektur ResNet-50 sebagai pengekstraksi fitur melalui teknik transfer learning dan LightGBM sebagai classifier. Menggunakan dataset GTZAN yang direpresentasikan dalam bentuk Mel-spectrogram, penelitian ini membandingkan efektivitas optimasi hyperparameter menggunakan metode Random Search dan Grid Search. Berdasarkan evaluasi kinerja, skenario hibrida dengan optimasi Grid Search terbukti menghasilkan kinerja terbaik dengan akurasi 81,20%, mengungguli metode Random Search. Kendati demikian, hasil eksperimen secara keseluruhan mengungkapkan bahwa model ResNet-50 end-to-end masih memberikan performa yang lebih unggul dibandingkan pendekatan hibrida. Hal ini mengindikasikan bahwa fitur mendalam dari ResNet-50 sudah sangat representatif untuk memisahkan kelas genre, sehingga penambahan classifier eksternal tidak memberikan peningkatan signifikan, meskipun pendekatan hibrida tetap menawarkan wawasan empiris penting sebagai model alternatif yang stabil. 
Pengembangan Sistem Pemantauan Distribusi Laporan Berbasis Web Kurniadi, Andry; Yasin, Verdi; Sianipar, Anton Zulkarnain
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 1 (2026): Februari 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i1.3439

Abstract

The Judicial Commission of the Republic of Indonesia faces challenges in monitoring and distributing public reports, which are still conducted manually and therefore lack effectiveness and integration. This study aims to develop a web-based Report Distribution Monitoring System Module to improve the effectiveness, efficiency, and transparency of report management within the Supervision Service Unit. The system was developed using the Rapid Application Development (RAD) method, which includes the stages of requirements planning, user design, construction, and cutover. The system was implemented as a web-based application using PHP as the programming language and MySQL as the database. System testing was carried out using black box testing involving Super Admin, Admin, and User roles to evaluate functional suitability. The test results indicate that all main system features operate in accordance with user requirements. Therefore, the developed system is considered feasible to support structured and integrated monitoring and distribution of public reports.Keywords: Judicial Commission; Bureaucratic Reform; Report Distribution Monitoring System Module; PHP; MySQLAbstrakKomisi Yudisial Republik Indonesia menghadapi tantangan dalam pemantauan dan pendistribusian laporan masyarakat yang masih dilakukan secara manual sehingga kurang efektif dan terintegrasi. Penelitian ini bertujuan mengembangkan Modul Sistem Pemantauan Distribusi Laporan Berbasis Web guna meningkatkan efektivitas, efisiensi, dan transparansi pengelolaan laporan pada Unit Layanan Pengawasan. Pengembangan sistem dilakukan menggunakan metode Rapid Application Development (RAD) yang meliputi tahap requirements planning, user design, construction, dan cutover. Sistem dikembangkan berbasis web menggunakan bahasa pemrograman PHP dan basis data MySQL. Pengujian sistem dilakukan melalui metode black box testing dengan melibatkan pengguna Super Admin, Admin, dan User untuk menguji kesesuaian fungsi sistem. Hasil pengujian menunjukkan seluruh fitur utama berjalan sesuai kebutuhan pengguna. Dengan demikian, sistem yang dikembangkan dinyatakan layak digunakan sebagai sarana pendukung pemantauan dan distribusi laporan masyarakat secara terstruktur dan terintegrasi. 

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