cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
+6282161108110
Journal Mail Official
jurnal.json@gmail.com
Editorial Address
STMIK Budi Darma Jln. Sisingamangaraja No. 338 Telp 061-7875998
Location
Kota medan,
Sumatera utara
INDONESIA
Jurnal Sistem Komputer dan Informatika (JSON)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : -     EISSN : 2685998X     DOI : https://dx.doi.org/10.30865/json.v1i3.2092
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
The Jurnal Sistem Komputer dan Informatika (JSON) is a journal to managed of STMIK Budi Darma, for aims to serve as a medium of information and exchange of scientific articles between practitioners and observers of science in computer. Focus and Scope Jurnal Sistem Komputer dan Informatika (JSON) journal: Embedded System Microcontroller Artificial Neural Networks Decision Support System Computer System Informatics Computer Science Artificial Intelligence Expert System Information System, Management Informatics Data Mining Cryptography Model and Simulation Computer Network Computation Image Processing etc (related to informatics and computer science)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 492 Documents
 39   40   41   42   43 
Sistem Pengelolaan Sampah Otomatis Integrasi Teachable Machine dan Robotik mBot 1 untuk Pengembangan Reverse Vending Machine Berkelanjutan Manalu, Rio Vincent Marulam; Pasaribu, Firdaus Hosea; Handinata; Napitupulu, Agrifa Insani; Agung Prabowo
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 1 (2025): September 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i1.9025

Abstract

Di Indonesia, masalah pengelolaan sampah masih menjadi perhatian utama, terutama yang berkaitan dengan sampah plastik dan kaleng. Tujuan dari proyek ini adalah untuk membuat prototipe Reverse Vending Machine (RVM) yang dapat secara otomatis mengenali dan menangani dua bentuk sampah yang berbeda dengan menggunakan Teachable Machine dan robotika mBot 1. Setelah mengambil gambar objek dengan kamera, sistem menggunakan model klasifikasi gambar dari Teachable Machine untuk memproses gambar tersebut. Untuk mengoperasikan aktuator servo yang memandu pembuangan objek, hasil klasifikasi dikirimkan ke mBot 1 melalui koneksi serial. Dengan akurasi pelatihan masing-masing 98% dan 97%, model ini dilatih pada dataset 200 foto, 100 di antaranya adalah botol plastik dan 100 foto lainnya adalah kaleng. Dengan total durasi eksekusi sekitar 7,2 detik, akurasi deteksi rata-rata dalam pengujian sistem adalah 85%. Hasil ini menunjukkan seberapa baik sistem dapat melakukan aktivitas fisik dan pendeteksian. Namun demikian, masalah seperti kesalahan klasifikasi karena penempatan barang yang salah dan penundaan komunikasi terus mengganggu sistem. Penelitian ini menunjukkan kemungkinan menggabungkan robotika dasar dan kecerdasan buatan ke dalam solusi pengelolaan sampah berteknologi. Hal ini juga dapat berfungsi sebagai alat pengajaran bagi siswa yang mempelajari otomasi dan AI.
Analisis Sentimen Opini Publik Terhadap 100 Hari Kinerja Prabowo Subianto Sebagai Presiden Menggunakan Naïve Bayes Khairunnisa; Sriani
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 1 (2025): September 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i1.9038

Abstract

Dalam kepemimpinan, kinerja seorang pemimpin dapat diukur menggunakan berbagai instrumen. Di masyarakat  kinerja dapat diukur dari hasil perubahan yang telah dilakukan seorang pemimpin. Prabowo subianto telah mengambil langkah yang signifikan dalam hal kepemimpinan. Maka opini masyarakat dibutuhkan guna mengevaluasi kinerjanya. Opini masyarakat memberikan beberapa pendapat yang berbeda di platform X. Metode yang digunakan adalah Naïve Bayes Classifier, yang mampu mengklasifikasikan data dengan menggunakan multinominal NB. Naïve bayes dirancang menggunakan bahasa pemrograman Python dan google collab sebagai tools. Tujuan penelitian ini mengetahui hasil opini yang diperoleh, maka analisis sentimen di perlukan terhadap kinerja prabowo subianto sebagai presiden berdasarkan akurasi. Dalam mengklasifikasikan sentimen terhadap kinerja prabowo, maka sentimen melalui proses crawling sebanyak 1000 data, kemudian split data, antara data latih dan data uji adalah 80:20, yaitu 800 data latih dan 200 data uji. Hasil pelabelan,  diketahui bahwa 588 tweet kategori sentimen positif dan 412 tweet kategori negatif. Hasil evaluasi dari metode naïve bayes memberikan hasil sentimen dengan nilai accuracy sebesar 74%, precision sebesar 67,57%, recall sebesar 64,10% dan F1 score sebesar 65,8%. Hal ini menunjukkan bahwa model memiliki performa yang cukup baik dalam mengklasifikasikan opini, meskipun efektivitas dalam mengidentifikasi komentar positif masih dapat ditingkatkan.
Website-Based Tourism Management Information System in Bahorok District Using Total Quality Management Method Decfina, Fauziah; Raissa Amanda Putri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 1 (2025): September 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i1.9069

Abstract

This research aims to develop a website-based tourism management information system in Bahorok District as a solution to the problem of tourism data management that is still carried out manually and has not been digitally integrated. The approach used in this study is the Research and Development (R&D) method with the Rapid Application Development (RAD) system development model, as well as applying the principles of Total Quality Management (TQM) to ensure the quality of the system from the design stage to implementation. The RAD method allows system development to be carried out quickly and flexibly through an iterative process that directly involves users in refining the design and functionality of the system. The development stages start from needs analysis, quick design, prototype development, system testing, to implementation. The developed system includes key features such as Home Page, Tour Page, Information Page, About Page, as well as admin features for Manage Tour Data and Manage Reviews. The system test was carried out through functionality tests, usability tests, and security tests, with results showing that the system functions optimally, is easy to use, and is able to improve the efficiency of management and access to tourist information by the public. The implementation of TQM can be seen from the active involvement of users in the development process, validation of the system based on real needs, and a focus on continuous improvement of the quality of the system's display and services. The final results of the study show that the information system built successfully achieved the research goal, which is to provide an informative, interactive, and support effective tourism management and promotion. This research also contributes to the application of quality management in the development of public sector information systems and can be a reference for similar developments in other regions or sectors.
Hybrid DAC-GA and K-Means for Spatial Clustering of Stunting Risk in North Sumatra Andy Satria; Ibnu Rusydi; Dian Septiana; Fanny Ramadhani
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 1 (2025): September 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i1.9071

Abstract

Stunting continues to pose a severe global health concern, particularly in Indonesia, where prevalence rates persist above international standards despite recent advances in reduction initiatives. Accurately documenting the regional variation of stunting is critical to facilitate targeted interventions and successful policymaking. This paper offers a hybrid clustering framework that merges the classic K-Means approach with the Dynamic Artificial Chromosomes Genetic approach (DAC-GA) to increase the resilience and reliability of spatial analysis. The dataset used combines demographic and population statistics from the Central Bureau of Statistics (BPS), strategic policy documents from the Regional Medium-Term Development Plan (RPJMD) of North Sumatra, and health indicators including stunting prevalence data from the Ministry of Health of the Republic of Indonesia. The research approach consists of four primary phases: data preparation, clustering model construction, cluster evaluation, and geographical visualization. Three evaluation metrics Sum of Squared Errors (SSE), Davies–Bouldin Index (DBI), and Silhouette Coefficient were applied to validate clustering performance. Results demonstrate that DAC-GA dynamically determined the ideal number of clusters at k=2 in just 1.171677 seconds, classifying Kota Medan and Deli Serdang into the low-risk cluster, while all other districts were consistently put into the high-risk cluster. Both DAC-GA and standard K-Means yielded similar spatial maps, giving significant methodological validation and strengthening the dependability of the findings. The study reveals not just the technical advantages of DAC-GA in maximizing clustering but also its practical utility in guiding spatially targeted health interventions. Future research is recommended to add dimensionality reduction utilizing Principal Component Analysis (PCA) to improve computing efficiency and enhance the interpretability of clustering results.
Determinant Analysis of Learning Interest in Informatics Management Students at STMIK Mulia Darma Sihombing, Monang Juanda Tua; Trianovie, Sri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 1 (2025): September 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to analyze the influence of intellectual intelligence, learning motivation, and learning behavior on the learning interest of students in the Information Management Study Program at STMIK Mulia Darma. Higher education institutions play a strategic role in improving the quality of human resources, particularly in shaping intellectual and professional abilities. However, various problems still exist, including low-quality educators, limited educational facilities, and weak links between education and the needs of the workforce. Intellectual intelligence without the support of positive motivation and learning behavior cannot produce an optimal learning process. Students with low motivation and learning behavior tend to show a lack of responsibility and participation in academic activities. This study uses the Structural Equation Modeling (SEM) approach with the Partial Least Squares (PLS) method to test the relationship between latent variables. The results of this study are expected to contribute to efforts to improve the quality of learning and develop student interest in learning in higher education.
LQ45 Index Stock Market Prediction: A Deep Learning Approach using LSTM with Bayesian Optimization Crisnapati, Padma Nyoman; Putu Devi Novayanti; Dian Pramana
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.8925

Abstract

This study investigates the application of Long Short-Term Memory (LSTM) models with Bayesian Optimization for predicting stock price movements in the LQ45 Index, a collection of the 45 most liquid stocks on the Indonesia Stock Exchange. The primary objective is to enhance prediction accuracy by addressing the challenges of volatile stock markets and inefficient hyperparameter tuning. Historical data, including daily closing prices from January 2020 to October 2024, was processed using Min-Max Scaling and transformed into time-series input features with a 60-time-step window. Bayesian Optimization was employed to fine-tune key hyperparameters such as LSTM units, dropout rate, and learning rate, optimizing the model's performance. The results revealed that the LSTM model accurately captured trends for stocks with stable price patterns, such as ACES, ASII, and MTEL, achieving low Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE). However, stocks with high volatility, like AMMN and ITMG, exhibited higher prediction errors, indicating limitations in modeling complex patterns. The study highlights that while LSTM with Bayesian Optimization is highly effective for stable stocks, additional preprocessing and advanced modeling techniques are required for volatile stocks. This research demonstrates the potential of machine learning in supporting stock market decision-making, contributing to the development of more robust and efficient financial prediction tools for investors navigating dynamic markets.
Model Prediktif Keterlambatan Pembayaran Mahasiswa Berbasis Seleksi Fitur dengan Particle Swarm Optimization Desvia, Yessica Fara; Suharjanti; Suhardjono; Irmawati Carolina; Resti Lia Andharsaputri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.8973

Abstract

Keterlambatan pembayaran biaya kuliah menjadi salah satu permasalahan krusial di perguruan tinggi swasta yang dapat berdampak pada risiko akademik, seperti cuti atau putus studi. Penelitian ini diarahkan untuk mengembangkan model prediktif dalam mengidentifikasi keterlambatan pembayaran oleh mahasiswa, dengan memanfaatkan algoritma klasifikasi Decision Tree dan Random Tree, serta menerapkan metode Particle Swarm Optimization (PSO) untuk proses seleksi fitur. Data yang digunakan dalam penelitian ini mencakup 15.697 mahasiswa, masing-masing memiliki enam atribut sebagai variabel prediktor serta satu atribut target yang menunjukkan status mahasiswa, yaitu aktif atau cuti. Tahapan penelitian mencakup pengumpulan data, pra-pemrosesan, klasifikasi, seleksi fitur, dan evaluasi model dilakukan dengan menggunakan metrik akurasi, serta kurva ROC dan nilai AUC. Hasil penelitian menunjukkan akurasi model mencapai 98,83%, dengan peningkatan signifikan AUC pada Random Tree dari 0,632 menjadi 0,825 setelah seleksi fitur menggunakan PSO. Temuan ini menunjukkan bahwa PSO efektif dalam meningkatkan performa model klasifikasi dan mengurangi kompleksitas fitur yang tidak relevan. Sistem prediktif yang dihasilkan dapat membantu institusi pendidikan dalam melakukan deteksi dini mahasiswa berisiko menunggak, sehingga memungkinkan pengambilan tindakan preventif dan intervensi lebih tepat sasaran untuk mendukung keberlangsungan akademik mahasiswa.
3D Game Asset Optimization Using Quadric Error Metrics And Decimate Modifier Arifudin, Dani; Khasanah, Nurul Afifatul; Robbani, Ayat Akras
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.8974

Abstract

3D asset optimization is a crucial challenge in game development because high-polygon models increase computational load, reduce frame rates, and negatively affect user experience in real-time rendering environments. This research addresses the problem by applying the Quadric Error Metrics (QEM) algorithm through the Decimate Modifier (Collapse Mode) in Blender as a solution for mesh simplification. Various high-polygon 3D models were simplified at different reduction levels and tested in the Unity engine to evaluate their impact on performance. The evaluation included frame rate measurement, vertex and triangle count reduction, and geometric accuracy assessment using the Hausdorff Distance method. The results show FPS improvements ranging from 9% to 41%, with vertex reduction between 8% and 33% and triangle reduction between 17% and 22%, while maintaining acceptable visual fidelity. These findings demonstrate that QEM-based optimization effectively balances visual quality and rendering performance, making it suitable for resource-constrained platforms such as mobile games and VR applications.
Klasifikasi Multikelas Tingkat Diabetes Berdasarkan Indikator Kesehatan Pasien Menggunakan Strategi One-vs-Rest Panjaitan, Tabitha Martha Agustine; Robet; Octara Pribadi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.8985

Abstract

Diabetes is a non-communicable disease with a steadily increasing global prevalence. It often remains undiagnosed in its early stages, particularly during the prediabetic phase, which typically lacks noticeable symptoms. This study aims to develop a multi-class classification model to predict diabetes severity levels non-diabetic, prediabetic, and diabetic based on patient health indicators. A One-vs-Rest (OvR) strategy was employed, training each class against a combination of the others. The dataset was derived from the 2015 National Health Survey, comprising over 250,000 patient records with features such as blood pressure, body mass index, cholesterol levels, history of heart disease, and physical activity. Two machine learning algorithms, Logistic Regression and Random Forest, were applied to train the models. Class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE). Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. The results show that the Random Forest model achieved an average accuracy of 93% and consistently high F1-scores, particularly for the prediabetic class of 98%. The most influential predictors were high blood pressure, obesity, and insufficient physical activity. This study contributes to the development of a reliable and efficient data-driven system for early diabetes risk detection.
PID-Controlled Gyroscopic Stabilization for Roll Balancing: A Simulation and Experimental Study Crisnapati, Padma Nyoman; Putu Devi Novayanti; Ni Ketut Ira Permata Adi; I Putu Agung Mas Aditya Warman; Anak Agung Istri Cintya Prabandari; I Made Darma Susila
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9001

Abstract

In marine engineering, stabilizing boat roll motion under wave-induced disturbances is a crucial problem where traditional approaches frequently have drawbacks in terms of responsiveness, energy efficiency, and adaptability. In this study, a PID-controlled gyroscopic stabilization system for boat roll balancing is designed, simulated, and experimentally validated. To capture the coupled dynamics of servo motor behavior, gyroscopic torque generation, and boat roll motion, a thorough dynamic model was created. Four gain configurations—Low, Moderate, High, and Very High—were evaluated using the model-guided PID parameter tuning that was implemented in Python. The mechanical system incorporates a gyroscopic flywheel driven by BLDC and mounted on a servo-controlled cradle. An ESP32 microcontroller processes real-time roll angle feedback from an MPU6050 sensor. According to simulation results, the ideal balance between rise time (~300 ms), overshoot (~2°), and settling time (~1 s) was reached with moderate PID gains. While High and Very High gains displayed instability because of unmodeled vibrations and sensor noise, a scaled physical prototype that was built and tested under controlled disturbances demonstrated strong alignment with simulation trends for Low and Moderate gains. The results show that moderate gains, which offer both quick stabilization and reliable performance, offer the most useful configuration for real-world applications. This work contributes a validated methodology for optimizing PID-controlled systems in dynamic environments by bridging the gap between theoretical modeling and practical implementation of marine gyroscopic stabilization.

Page 41 of 50 | Total Record : 492

 39   40   41   42   43 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue Vol. 7 No. 3 (2026): Maret 2026 Vol. 7 No. 2 (2025): Desember 2025 Vol. 7 No. 1 (2025): September 2025 Vol 6, No 4 (2025): Juni 2025 Vol. 6 No. 4 (2025): Juni 2025 Vol. 6 No. 3 (2025): Maret 2025 Vol 6, No 3 (2025): Maret 2025 Vol 6, No 2 (2024): Desember 2024 Vol. 6 No. 2 (2024): Desember 2024 Vol. 6 No. 1 (2024): September 2024 Vol 6, No 1 (2024): September 2024 Vol 5, No 3 (2024): Maret 2024 Vol. 5 No. 3 (2024): Maret 2024 Vol. 5 No. 2 (2023): Desember 2023 Vol 5, No 1 (2023): September 2023 Vol 4, No 4 (2023): Juni 2023 Vol 4, No 3 (2023): Maret 2023 Vol 4, No 2 (2022): Desember 2022 Vol 4, No 1 (2022): September 2022 Vol 3, No 4 (2022): Juni 2022 Vol 3, No 3 (2022): Maret 2022 Vol 3, No 2 (2021): Desember 2021 Vol 3, No 1 (2021): September 2021 Vol 2, No 3 (2021): Mei 2021 Vol 2, No 2 (2021): Januari 2021 Vol 2, No 1 (2020): September 2020 Vol 1, No 3 (2020): Mei 2020 Vol 1, No 2 (2020): Januari 2020 Vol 1, No 1 (2019): September 2019 More Issue