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All Journal KOMPUTIKA - Jurnal Sistem Komputer JUSIFOR : Jurnal Sistem Informasi dan Informatika Applied Information Technology and Computer Science (AICOMS) Indonesian Journal of Infomatics Computer Architecture and Signal Processing Software Engineering in Computing Systems
Hidayat Hidayat
Universitas Komputer Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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Sistem Informasi Penjualan Tahu Berbasis Website (Studi Kasus: Pabrik Tahu Tiga Bola) Neng Nelly Nurul Arsy; Tyara Eka Widhia Priatna; Hadiyan Fadhilah Nugraha; Hidayat Hidayat
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 3 No 2 (2024): JUSIFOR - Desember 2024
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v3i2.5524

Abstract

Penelitian ini bertujuan merancang dan mengimplementasikan sistem informasi penjualan berbasis website untuk Pabrik Tahu Tiga Bola, yang masih menggunakan metode penjualan dan pencatatan konvensional, sehingga jangkauan pembeli terbatas dan rentan terjadi kesalahan. Metode yang digunakan adalah Waterfall dengan enam tahapan: analisis, perancangan, implementasi, pengujian, penerapan, dan pemeliharaan. Sistem ini dirancang menggunakan framework Laravel dan MySQL, serta HTML5, CSS3, Bootstrap, dan Javascript untuk antarmuka web. Hasil penelitian menunjukkan bahwa sistem informasi penjualan dapat meningkatkan efisiensi operasional melalui otomatisasi pengelolaan data produk, penjualan, dan pelanggan, serta memudahkan konsumen melakukan transaksi. Aplikasi ini diharapkan membantu Pabrik Tahu Tiga Bola memperluas jangkauan pasar dan lebih responsif terhadap kebutuhan konsumen.
Prediksi Penyakit Liver Dengan Model Pebandingan Logistic Regression, Support Vector Machine, Dan Random Forest Hidayat Hidayat; Mochamad Fajar Wicaksono; Yudis Jalu Wicaksono; Sandy Maulana Wibawa; Edvan Nuriana Putra Septia
Komputika : Jurnal Sistem Komputer Vol. 15 No. 1 (2026): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v15i1.19799

Abstract

Penyakit liver sering berkembang tanpa gejala awal yang jelas sehingga menyulitkan proses deteksi dini dan meningkatkan risiko komplikasi serius. Keterbatasan analisis manual terhadap data laboratorium pasien mendorong perlunya pendekatan komputasi yang mampu memberikan klasifikasi penyakit secara lebih akurat dan konsisten. Pendekatan pembelajaran mesin diterapkan untuk mengklasifikasikan potensi penyakit liver menggunakan data klinis terstruktur. Kontribusi utama yang dihasilkan berupa evaluasi komprehensif tiga algoritma klasifikasi dengan pengujian internal serta validasi eksternal menggunakan data yang tidak terlibat dalam pelatihan. Dataset yang digunakan terdiri dari 579 data pasien yang telah dibersihkan dan dibagi menjadi data pengembangan serta data uji akhir. Pemodelan dilakukan menggunakan algoritma regresi logistik, hutan acak, dan mesin vektor pendukung dengan variasi pembagian data latih sebesar 60%, 70%, dan 80% melalui pengambilan sampel acak berulang. Hasil pengujian menunjukkan bahwa metode hutan acak menghasilkan performa terbaik dengan nilai F1 sebesar 0,696 dan koefisien korelasi Matthews sebesar 0,203 pada pembagian data 80%. Regresi logistik menunjukkan akurasi stabil sebesar 0,728 namun memiliki kemampuan generalisasi yang lebih rendah. Mesin vektor pendukung menghasilkan akurasi cukup tinggi tetapi menunjukkan ketidakseimbangan klasifikasi. Pengujian data baru menunjukkan bahwa metode hutan acak mencapai kecocokan prediksi sebesar 98%, tertinggi di antara seluruh metode. Hasil tersebut menunjukkan bahwa metode hutan acak lebih efektif dalam klasifikasi penyakit liver karena memberikan keseimbangan antara akurasi, stabilitas, dan kemampuan generalisasi model.
The Effect of Data Imbalance on the Interpretation Stability of LIME-Based Explainable AI on Nutritional Status Prediction Models Sri Nurhayati; Hidayat Hidayat; Siti Ar-Rachmi Ningrum; Zainal Arifin Hasibuan; Sri Supatmi
Indonesian Journal of Infomatics Vol. 1 No. 2 (2026): May: Indonesian Journal of Infomatics
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/iji.v1i2.438

Abstract

Data imbalance is a common challenge in nutritional status prediction because it can reduce classification performance and influence the reliability of Explainable Artificial Intelligence (XAI) interpretations. This study aims to examine the impact of data imbalance on the stability of Local Interpretable Model-Agnostic Explanations (LIME)-based interpretations. A Random Forest model was developed under two scenarios: using the original imbalanced dataset and using a balanced dataset generated through the Synthetic Minority Over-sampling Technique (SMOTE). Model performance was evaluated and compared, followed by LIME-based interpretation and stability analysis. The results indicate that SMOTE enhanced the model’s ability to identify minority classes, with recall increasing from 0.36 to 0.55, although overall accuracy slightly declined. LIME analysis revealed changes in feature contributions between the two scenarios, reflecting the influence of data distribution on model explanations. The interpretation stability score reached 0.80, suggesting relatively consistent explanations despite variations in class balance. These findings highlight the importance of jointly evaluating predictive performance and interpretation stability in health-related machine learning applications.
Adaptive-Cognitive Smart Farming Architectures for Food Security Resilience: A Systematic Literature Review of IoT and AI-Based Approaches Ridho Taufiq Subagio; Zainal Arifin Hasibuan; Bobby Kurniawan; Sri Supatmi; Hidayat Hidayat; Citra Noviyasari
Computer Architecture and Signal Processing Vol. 1 No. 2 (2026): June: Computer Architecture and Signal Processing
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/casp.v1i2.445

Abstract

Food security resilience has become an increasingly critical global concern due to the combined effects of climate change, population growth, and resource scarcity. Conventional agricultural practices are no longer sufficient to meet rising food demands, thereby necessitating the adoption of intelligent and adaptive technological solutions. Smart farming, enabled by the integration of the Internet of Things (IoT) and Artificial Intelligence (AI), has emerged as a promising approach to enhance agricultural productivity, efficiency, and sustainability. However, existing smart farming systems remain fragmented and lack adaptive and cognitive capabilities required to dynamically respond to environmental variability. This study proposes an adaptive-cognitive smart farming architecture that integrates IoT, AI, edge-fog-cloud computing, federated learning, and digital twin technologies into a unified framework. A Systematic Literature Review (SLR) is conducted to synthesize insights from 60 high-quality publications indexed in IEEE, Elsevier, and Scopus databases. The proposed architecture adopts a multi-layered design consisting of sensing, edge-fog, cloud, cognitive, and application layers, enabling real-time data processing, distributed intelligence, and adaptive decision-making. To validate the proposed model, experimental simulations are performed using key performance indicators, including accuracy, mean squared error (MSE), latency, and resource efficiency. The results indicate that the proposed approach achieves superior performance, with an accuracy of 89%, a substantial reduction in latency, and improved resource utilization. These findings demonstrate that incorporating adaptive and cognitive intelligence significantly enhances system responsiveness and decision-making capabilities. This study contributes to both theory and practice by introducing a comprehensive framework for next-generation smart farming systems, ultimately supporting food security resilience in an increasingly uncertain environment.
The Integration Of Non-Academic Variables In Student Risk Assessment: A Conceptual Framework Hani Irmayanti; Eddy Soeryanto Soegoto; Hidayat Hidayat; Rio Yunanto; Zainal Arifin Hasibuan; Sri Supatmi
Software Engineering in Computing Systems Vol. 1 No. 2 (2026): May: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i2.435

Abstract

Students’ success in completing their studies on time is a vital indicator of the quality of higher education management in Indonesia. However, high dropout rates pose a major challenge, often caused by institutions’ failure to detect warning signs of academic failure in a timely manner. The main issue lies in the current evaluation approach, which is reactive and limited to conventional academic indicators such as the Grade Point Average (GPA), thereby neglecting the psychosocial factors that influence performance. This study aims to develop a more comprehensive conceptual framework for the early detection of academic failure risk by integrating academic and non-academic dimensions. The methodology used is adapted from the Design Science Research Methodology (DSRM), focusing on the stages from problem identification to the design of the model artifact. The proposed approach is a hybrid model that combines traditional academic variables with non-academic variables, including psychological stress levels, self-efficacy, and social support. The design results indicate that this framework is capable of identifying “latent pressure” as a leading indicator of failure before a decline in academic performance occurs. The synthesis of this study confirms that the integration of non-academic variables enhances the model’s transparency and provides a more meaningful and targeted interpretation of risk factors. In conclusion, this framework provides a theoretical foundation for educational institutions to transition from reactive evaluation to a system of personalized, proactive interventions. The implementation of this model is expected to improve student retention through earlier and more targeted risk mitigation.
Rancang Bangun Game Edukasi Interaktif sebagai Media Pembelajaran Mufrodat Bahasa Arab Sekolah Dasar (Studi Kasus: SD Al-Ihsan Islamic School) Ilham Assidiq; Hidayat Hidayat
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i2.1815

Abstract

Effective learning requires innovation to enhance student engagement and understanding of the material. This study aims to develop an educational game for learning mufrodat (Arabic vocabulary) for fifth-grade elementary school students using Unity 3D and the Game Development Life Cycle (GDLC) approach. The GDLC method consists of five stages: concept, pre-production, production, testing, and release, ensuring a systematic game development process. The game is designed to provide an interactive and enjoyable learning experience, enabling students to acquire Arabic vocabulary through gameplay activities. Unity 3D was chosen for its ability to create dynamic and interactive game environments. The testing results indicate that the developed educational game is effective in enhancing students’ understanding and retention of Arabic vocabulary, with positive feedback received from both students and teachers. Future development may include the addition of more comprehensive learning features and adaptations for various levels of difficulty.
Sistem Pemantauan Detak Jantung Berbasis ESP32 Menggunakan Sensor AK90 Dengan Antarmuka Web Aqsa Chalik; Hidayatul Rohman; Luthfia Rosdiansyah; Alief Fazrian; Hidayat Hidayat
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i2.1958

Abstract

Heart rate monitoring plays a crucial role in maintaining cardiovascular health, enabling early detection of disorders such as arrhythmia. This study aims to develop an Internet of Things (IoT)-based heart rate monitoring system using the AK90 sensor and ESP32 microcontroller integrated with a web interface to support real-time monitoring and digital data storage. The system was developed using the Waterfall approach, consisting of requirement analysis, system design, implementation, testing, and deployment in a Posyandu environment. The AK90 sensor utilizes the principle of photoplethysmography to detect heart rate, with data processed by the ESP32, displayed on an OLED screen, and stored via a local web server. Testing on four subjects showed that the system was able to record heart rates within the normal range (60–100 BPM) after a 10-second sensor stabilization period, although initial readings often showed a value of 0 due to the initialization process. This system offers a practical, affordable, and standalone solution for heart health monitoring, with potential for future enhancements such as notifications and historical data analysis to support medical diagnosis.
Co-Authors Alief Fazrian Aqsa Chalik Bobby Kurniawan Citra Noviyasari Eddy Soeryanto Soegoto Edvan Nuriana Putra Septia Hadiyan Fadhilah Nugraha Hani Irmayanti Hidayatul Rohman Ilham Assidiq Luthfia Rosdiansyah Mochamad Fajar Wicaksono Neng Nelly Nurul Arsy Ridho Taufiq Subagio Rio Yunanto Sandy Maulana Wibawa Siti Ar-Rachmi Ningrum Sri Nurhayati Sri Supatmi Tyara Eka Widhia Priatna Yudis Jalu Wicaksono Zainal Arifin Hasibuan