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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) International Journal Software Engineering and Computer Science (IJSECS) Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Saputra, Tino
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Physics

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 1 
Analysis of the Effectiveness of IoT-Based Automatic Street Lighting Control Using Linear Regression Method Saputra, Tino; Surapati, Untung
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2878

Abstract

Public street lighting (PJU) is a crucial component of infrastructure that ensures security during nighttime. This research aims to design an automatic PJU control system utilizing Internet of Things (IoT) technology, employing light and motion sensors integrated with an ESP32 microcontroller. The system enables remote control of PJU lamps via a web-based platform, offering significant flexibility for users. The ESP32 microcontroller is linked to a PIR sensor that detects motion, which triggers an increase in the intensity of the PJU lamps. Conversely, when no motion is detected, the light intensity is reduced to conserve energy. Users can manage the PJU lamps from any internet-connected device. Experimental results demonstrate a notable improvement in energy efficiency, with an average reduction in power consumption of 13.77 watts and an efficiency increase of 42.67%. The linear regression model employed yields an R-squared value of 0.629, indicating a reasonably good fit in explaining the variability in power consumption. This system offers real-time monitoring and autonomous operation of street lights, contributing to the advancement of smarter and more efficient PJU systems.
Analisis Efektivitas Sistem Kendali Otomatis PJU Berbasis IoT Menggunakan Mikrokontroler ESP32 dengan Metode Regresi Linier Saputra, Tino; Surapati, Untung
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.932

Abstract

Public street lighting (PJU) is essential infrastructure for nighttime security. This research develops an automatic PJU control system based on IoT using light and motion sensors with an ESP32 microcontroller. The system allows for PJU lamp control via a website, providing high flexibility for users. The ESP32 microcontroller is connected to a PIR sensor to detect motion. When the sensor detects movement, the intensity of the PJU lamps is increased, and when no movement is detected, the light intensity is reduced to save energy. Users can control the PJU lamps from any internet-connected device. Testing results show an increase in energy efficiency, with an average power consumption reduction of 13.77 watts and an efficiency increase of 42.67%. An R-squared value of 0.629 indicates that the model is quite good at explaining the variability in power consumption data. This system can automatically turn on street lights and be monitored in real-time, And hopes to contribute to the development of smarter and more efficient street lighting systems.
Analisis Algoritma Machine Learning untuk Prediksi Parameter Flue Gas pada PLTU Batubara: Studi Literatur 2019–2024 Saputra, Tino; Munawar, Munawar
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 1 (2026): JANUARY 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i1.5083

Abstract

Improving combustion efficiency and reducing flue gas emissions in coal-fired power plants (CFPPs) have become critical priorities amid growing global pressure to mitigate the environmental impact of the energy sector. Machine learning (ML) has demonstrated strong potential in predicting flue gas parameters, yet systematic reviews mapping algorithmic trends, implementation challenges, and integration opportunities with CFPP operations remain limited. This study presents a systematic literature review (SLR) of 31 selected articles published between 2019 and 2024 across Scopus, IEEE Xplore, and ScienceDirect databases, utilizing the PICOC framework for selection. The analysis shows that LSTM is the most frequently applied model for temporal prediction of flue gas temperature, while Random Forest is widely adopted for estimating NOx emissions. However, most studies are constrained to single-plant datasets, and real-time control system integration is still uncommon. These findings highlight the need for hybrid approaches that emphasize not only predictive accuracy, but also model interpretability via Explainable AI methods such as SHAP, and adaptability across diverse operational conditions. This study advocates future development directions by embedding predictive models within digital twin frameworks to enhance decision-making and optimize system performance sustainably. As such, the review contributes to bridging academic research with practical industrial demands in coal-based energy generation.
Explainable Brain Tumor Classification Using EfficientNet-B2 and Grad-CAM on MRI Dataset　 Saputra, Tino; Magribi, Wahyu Purnama; Tundjungsari, Vitri
Jurnal Penelitian Pendidikan IPA Vol 12 No 3 (2026): In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i3.14179

Abstract

Brain tumors are life-threatening central nervous system disorders requiring early and accurate diagnosis for effective clinical management. Although MRI is the standard modality for detection, manual interpretation remains prone to inconsistency, particularly for complex cases such as glioma. This study proposes an explainable deep learning framework integrating EfficientNet-B2 with a threshold-based two-stage classification scheme and Grad-CAM interpretability analysis. In the first stage, a one-versus-rest binary classifier with an optimized threshold (τ = 0.20) performs glioma detection; the second stage classifies remaining cases into meningioma, pituitary tumor, or normal. The dataset comprises 7,023 MRI images across four classes from a public Kaggle repository. Preprocessing includes CLAHE contrast enhancement, normalization, and augmentation. EfficientNet-B0 serves as the baseline. EfficientNet-B2 achieves 97.9% overall accuracy, outperforming the baseline (96.7%), with a glioma F1-score of 0.988 at the optimal threshold. Grad-CAM visualizations confirm the model focuses on anatomically relevant regions, enhancing transparency and clinical trustworthiness. The proposed framework demonstrates that combining architectural capacity, threshold-based inference, and explainability yields a reliable system for computer-aided brain tumor diagnosis.
Data Mining for Predicting Creditworthiness in Credit Card Approval: A Systematic Literature Review Purnama Magribi, Wahyu; Fazly Qusyairy, Muhammad; Saputra, Tino
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6618

Abstract

The growing volume of credit card applications has led financial institutions to seek faster and more reliable methods in the approval process. Manual evaluation is not only time-consuming but also susceptible to human error, which can result in poor credit decisions and measurable financial losses. This study conducts a Systematic Literature Review (SLR) to examine data mining techniques applied to creditworthiness prediction. Five research questions were formulated to identify: (1) commonly used data mining techniques, (2) frequently used datasets, (3) performance evaluation metrics, (4) algorithms with the strongest performance, and (5) recurring challenges and practical recommendations. A structured search across three academic databases — Scopus, Google Scholar, and GARUDA — yielded 8 relevant articles (7 primary experimental studies and 1 secondary study) published between 2021 and 2025. The findings show that Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbors are the most widely applied methods. Tree-based algorithms such as Decision Tree and Random Forest consistently yield high accuracy, while K-Nearest Neighbors also delivers strong results in specific experimental settings. Naïve Bayes appears most frequently across studies, and its performance can be improved through metaheuristic approaches such as Particle Swarm Optimization (PSO). Standard evaluation metrics include accuracy, precision, recall, F1-score, and AUC-ROC. The review underscores the importance of data preprocessing, class imbalance handling, and hyperparameter tuning in building reliable prediction models — findings with direct implications for financial institutions seeking to reduce non-performing loan rates.
Co-Authors Fazly Qusyairy, Muhammad Magribi, Wahyu Purnama Munawar Munawar Purnama Magribi, Wahyu Surapati, Untung Vitri Tundjungsari