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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Manajemen Informatika Jurnal Algoritma Eksergi: Chemical Engineering Journal JAIS (Journal of Applied Intelligent System) JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology Sebatik Jurnal ULTIMATICS KOMPUTIKA - Jurnal Sistem Komputer METIK JURNAL Building of Informatics, Technology and Science Jurnal Pengabdian Masyarakat Sains Indonesia (Indonesian Journal Of Science Community Services) International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) JoMMiT : Jurnal Multi Media dan IT Jurnal Algoritma Journal of Computing Theories and Applications NERO (Networking Engineering Research Operation) Journal of Multiscale Materials Informatics Jurnal Inovasi Pendidikan dan Pembelajaran
Akrom, Muhamad
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Physics Public Health Social Sciences

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Journal : Building of Informatics, Technology and Science

 1 
Optimizing Quantum Neural Networks for Predicting the Effectiveness of Drug Compounds as Corrosion Inhibitors Mawaddah, Lubna; Rosyid, Muhammad Reesa; Santosa, Akbar Priyo; Akrom, Muhamad
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5318

Abstract

Corrosion, caused by electrochemical reactions in corrosive environments, can degrade the quality and lifespan of materials, potentially leading to significant losses in various industrial sectors. One common strategy to reduce corrosion rates is by using corrosion inhibitors. A significant challenge in this field is the time-consuming and costly process of testing new corrosion inhibitors in the laboratory. Consequently, there is a need for more efficient and cost-effective methods to predict the effectiveness of potential corrosion inhibitors using machine learning techniques. This research addresses this problem by applying a quantum machine learning (QML) approach with quantum neural network (QNN) algorithms to evaluate the effectiveness of drug compounds as corrosion inhibitors. The study aims to optimize QNN models by investigating three different quantum circuit configurations to identify the most effective design. The results showed that Model-01, consisting of three layers, demonstrated the best performance with an MSE of 38.81, an RMSE of 6.23, and an MAE of 6.19, along with the shortest training time of 32 seconds, indicating an optimal balance between complexity and generalizability. Overall, this QML approach provides new insights into the predictive ability of QNN models in assessing the effectiveness of drug compounds as corrosion inhibitors, demonstrating the potential of quantum computing to enhance predictive accuracy and efficiency in investigating anti-corrosion materials
Analisis Komparatif Arsitektur Convolutional Neural Network untuk Klasifikasi Kualitas Cabai dengan Implementasi Perangkat Mobile Ikhsanudin, Nur; Akrom, Muhamad
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9419

Abstract

Manual chili quality sorting is susceptible to subjectivity and inter-assessor inconsistency, which can reduce product market value. This study conducts a comparative analysis of three Convolutional Neural Network (CNN) architectures—Custom CNN, MobileNetV3-Small, and EfficientNetV2-B0 for binary chili quality classification (Good/Bad) using a primary dataset of 1,383 chili images (684 Good-class, 699 Bad-class) captured with a smartphone camera. The Good class includes chili with a smooth surface, fresh color, and no decay spots, while the Bad class includes chili showing signs of decay, physical defects, or deformation. Evaluation was conducted based on accuracy, precision, recall, F1-Score, AUC, inference time, and post-quantization model size. The results show that EfficientNetV2-B0 achieved the highest accuracy of 92.0% (precision 92.4%, recall 92.0%, F1-Score 92.0%, AUC 0.961), MobileNetV3-Small obtained an accuracy of 87.7% with the lowest server-side inference latency (2.39 ms), and Custom CNN achieved 87.3% accuracy with the most compact model size (118 KB post-quantization). All three models were integrated into a Flutter-based Android application prototype as a proof-of-concept, displaying the classification result (Good/Bad), confidence score, and inference latency, with end-to-end response times ranging from 80 to 120 ms on a Xiaomi 13T device. This study contributes empirical comparative data on three CNN architectures in the chili quality classification domain, accompanied by the construction of a local dataset and technical validation of model deployment on a mobile device. The results of this study are expected to serve as a reference in selecting CNN architecture for the development of a mobile-based chili quality classification system, particularly as a first step toward the implementation of simple small-scale sorting at the farmer level.
Analisis Performa K-Nearest Neighbor dengan Optimasi F1-Score dan Teknik SMOTE dalam Klasifikasi Risiko Serangan Jantung Pratama, Fikri Luqman; Akrom, Muhamad
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9493

Abstract

Heart attack is one of the leading causes of death worldwide, making early risk prediction essential for improving patient outcomes. However, many medical datasets suffer from class imbalance, where the number of high-risk cases is significantly smaller than normal cases. This condition may cause machine learning models to be biased toward the majority class and reduce their ability to detect high-risk patients. This study aims to analyze the performance of the K-Nearest Neighbor (KNN) algorithm optimized using F1-score and combined with the Synthetic Minority Over-sampling Technique (SMOTE) for heart attack risk classification. The dataset used is the Heart Attack Dataset, which consists of numerical and categorical features. The research applies an experimental approach by developing a machine learning pipeline that includes data preprocessing, missing value handling, feature standardization, oversampling using SMOTE, and hyperparameter optimization through GridSearchCV with F1-score as the main evaluation metric. Model evaluation is conducted using Stratified 5-Fold Cross-Validation with accuracy, precision, recall, F1-score, and ROC-AUC metrics. The results show that the baseline KNN model achieves an accuracy of 98.50%, precision 95.27%, recall 81.47%, and ROC-AUC 0.9278. Meanwhile, the KNN model integrated with SMOTE attains a recall of 87.27% and ROC-AUC of 0.9484, indicating improved detection of heart attack cases and a reduction in false negatives by 31%, although precision decreases to 72.15%. These findings demonstrate that the integration of SMOTE and hyperparameter optimization effectively improves model sensitivity, making it more suitable for medical applications that prioritize patient safety.
Co-Authors Achmad Wahid Kurniawan Adiprasetya, Vicenzo Frendyatha Aldiansah, Ilham Pratama Alfa Trisnapradika, Gustina Amri, Sahrul Ardyansyah, Nibras Bahy Ariyanto, Noval Ayu Pertiwi Budi, Setyo De Rosal Ignatius Moses Setiadi Desrina Desrina Diah Rahayu Ningtias, Diah Rahayu Dian Arif Rachman Ditha Sabrani, Amalia Gumelar, Rizky Syah habiburrahman, Lalu Hariati, Endang Harun Al Azies Herawati, Wise Herowati, Wise Hidayat, Ari Hidayat, Novianto Hidayat, Novianto Nur Hilmy, Nur Amalina Rahmaputri HOTIMAH, NURUL Huryati, Huryati Husnawati Amini, Titik Ignasius, Darnell Ikhsanudin, Nur Islahudin Jalaluddin Jalaluddin Jannah, Rauhun Kurniawan, Azril Lingga Dewi, Adhe Mawaddah, Lubna Miyati, Hijiul Moch Anjas Aprihartha Muhamad Fadil Muhammad Naufal Muhammad Sanusi Mulyana, Yudha Musafir, Musafir Noor Ageng Setiyanto, Noor Ageng Nur Safitri, Aprilyani Oktaviansyah, Brenendra Putra Prabowo, Wahyu Aji Eko Pratama, Fikri Luqman Putra, Dzaki Asari Surya Putranto, Nicholaus Verdhy Rachmat Imam Muslim Rahman, Irfan Fauzia Rana, Bastion Jader Ristina Wahyu Astuti Rizky Pratama, Muhammad Hafiz Rosyid, Muhammad Reesa Safitri, Aprilyani Nur Sahrul Kadri, Agus Santosa, Akbar Priyo setiawan, nabila putri Sheilla Rully Anggita Sudibyo, Usman Sumarjono, Cornellius Adryan Putra Supriadi Rustad T. Sutojo Trinapradika, Gustina Alfa Trisnapradika, Gustina Alfa Umam, Saripul Widia Wati, Via Yani, Rindi Zihab, Zihab Zulfiani, Nila