Article Per Year (5 Year)
p-Index From 2021 - 2026
0.92
P-Index
This Author published in this journals
All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research
Gunadi Widi Nurcahyo
Universitas Putra Indonesia YPTK Padang, Padang
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Analisis Kepuasan Masyarakat terhadap Layanan KUA Menggunakan Algoritma K-Means dan C4.5 Nabilah Putri Permana; Agung Ramadhanu; Gunadi Widi Nurcahyo
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9324

Abstract

The Office of Religious Affairs (KUA) is an institution under the Ministry of Religious Affairs that provides religious services to the community, including marriage administration. Improving the quality of public services requires data-driven evaluation to measure the level of public satisfaction with the services provided. This study aims to analyze the level of community satisfaction with the services of the Office of Religious Affairs in Tebing Tinggi District using a combination of the K-Means Clustering and C4.5 algorithms. The research data were obtained from questionnaires distributed to community members who used KUA services. The K-Means algorithm was applied to group community satisfaction data based on the similarity of attribute values, while the C4.5 algorithm was used to build a classification model that generates decision rules to predict the level of community satisfaction. The results show that the proposed methods are able to group satisfaction levels in a structured manner and produce a classification model with high accuracy in analyzing public service satisfaction. The findings of this study are expected to support KUA in evaluating and improving service quality, as well as provide a reference for the application of data mining techniques in analyzing community satisfaction in public service sectors.
Model Deep Learning Berbasis Multilayer Perceptron untuk Identifikasi Demam Berdarah Dengue dan Tifus Nurhadi Nurhadi; Sarjon Defit; Gunadi Widi Nurcahyo
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.754

Abstract

Dengue Hemorrhagic Fever (DHF) and Typhus/Typhoid are two infectious diseases often found in tropical areas. In Indonesia, data shows that cases of DHF and typhoid are quite high, so a system is needed that can help doctors make faster and more accurate decisions based on blood test results. Based on the previous explanation, this study aims to apply the Deep Learning Multilayer Perceptron (MLP) method to be able to identify dengue fever and typhus. This study uses a Deep Learning-based Multilayer Perceptron approach for accurate classification of Dengue Fever, Typhoid Fever, and Normal cases using clinical blood parameters and selected symptoms. This methodology consists of several stages: dataset acquisition, preprocessing, model architecture design, training, and evaluation. The dataset was taken from Dumai City Hospital medical record data from 2023 to 2024, totaling 379 patient data used to identify Dengue Fever and Typhus using 7 clinical parameters as the main input obtained from laboratory examination results and patient clinical symptoms: Hemoglobin, Leukocyte, Platelet count, Hematocrit level, Headache, Abdominal pain, and diarrhea. Based on the results obtained, the application showed the best performance in classifying Dengue Fever, which is shown through the achievement of the model evaluation metrics as follows. The test results indicate that an increase in the amount of test data is directly proportional to the percentage of classification success achieved by the system. Based on the test results with 10% validation data, 70 % training data, and 20 % test data, the system showed very good performance with an overall accuracy of: 98.68% (Accuracy = 0.9868), which indicates a high level of success in classifying for the three classes, namely Normal, Dengue Fever, and Typhus.
Analisis Algoritma K-Means Clustering untuk Pengelompokan Rekomendasi Judul Proposal Tugas Akhir Mahasiswa Sandra Yulihartati; Sarjon Defit; Gunadi Widi Nurcahyo
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.755

Abstract

The academic process requires speed and accuracy in processing student data, such as submitting final project titles. In the context of final project title recommendations, many universities have not yet implemented the Data Mining approach optimally. Based on this, this study aims to recommend grouping of student final project proposal titles. The K-Means clustering method can be used in grouping data based on similarities between analyzed objects. With the K-Means method, the student grouping process utilizes grade data from the courses of Rock Mechanics, Drilling and Excavation Techniques, Underground Mining Methods, Reserve Modeling and Evaluation, Explosives and Blasting Techniques, Open Pit Mining, Mine Drainage Systems, Mapping Surveys, and Mineral Resources. The results of K-Means are strongly influenced by the k parameter and centroid initialization. The research variables include data mapping of course grades of students in the Mining Engineering Study Program. Based on the K-Means Clustering Method, it has been able to divide 104 student value data into 3 clusters, namely Natural Resource Exploration (C0), Geomechanics (C1) and Mining Environment (C2). The results of Cluster CO are 60, the results of Cluster C1 are 27 and the results of Cluster C2 are 17. The contribution of this research can provide fast, precise and accurate information in grouping recommendations for student final project proposal titles.
Analisis Metode Forward Chaining dan Certainty Factor untuk Diagnosa Penyakit pada Ibu Hamil Nabilla Yasmin; Yuhandri Yuhandri; Gunadi Widi Nurcahyo
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.756

Abstract

The high number of complications that occur during pregnancy and childbirth has the potential to significantly increase the risk of morbidity and mortality in pregnant women. The Maternal Mortality Rate (MMR) reflects the condition of pregnant, delivering, and postpartum mothers, which remains relatively high and is a major concern in the health sector. Based on this, this study aims to develop and evaluate an Expert System based on the Forward Chaining and Certainty Factor methods to diagnose diseases in pregnant women at an early stage, thereby providing fast and accurate medical decision support and minimizing the risk of complications during pregnancy. The Forward Chaining and Certainty Factor methods were chosen for their ability to handle rule-based inference processes and provide certainty level calculations in the diagnosis results. Forward Chaining is used to find solutions based on the symptoms entered by users, while the Certainty Factor helps assign confidence weights to the generated diagnosis. The dataset in this study consists of 30 data samples with 30 types of symptoms experienced by patients as variables. The results show that the Forward Chaining and Certainty Factor methods are capable of producing disease diagnoses in pregnant women with an accuracy rate of 95%. The contribution of this research is to improve the quality of maternal health services through fast and accurate diagnoses by medical personnel and to assist pregnant women in obtaining an initial diagnosis of common diseases during pregnancy.
Integrasi Principal Component Analysis dan Logistic Regression untuk Analisis Sentimen Kepuasan Pelanggan Berdasarkan Ulasan Online Tsalsabila Jilhan Haura; Rini Sovia; Gunadi Widi Nurcahyo
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1029

Abstract

Customer reviews on digital platforms are an important source of information for evaluating service quality and customer satisfaction levels. However, the unstructured nature of review data and its high feature dimensionality pose challenges in the sentiment analysis process. This study aims to develop a customer sentiment analysis model by integrating Principal Component Analysis (PCA) and Logistic Regression. The data used are 679 Indonesian-language reviews obtained through web scraping techniques from Google Reviews at ten d'Besto EBM branches in Padang City. The research stages include text preprocessing, TF-IDF weighting, dimensionality reduction using PCA, and sentiment classification using Logistic Regression. The results show that PCA is able to reduce data complexity by producing two principal components that explain 85.7% of the total data variance. The Logistic Regression model built on the features resulting from PCA reduction achieved an accuracy of 82%, demonstrating the model's ability to effectively classify positive and negative sentiments. In addition to improving computational efficiency, the use of PCA also helps reduce feature redundancy in high-dimensional text data. The contribution of this research is to produce a simpler and more efficient sentiment analysis approach to process customer reviews and provide data-based information that can be used to support service quality evaluation and decision-making in the culinary industry.
Co-Authors Agung Ramadhanu Nabilah Putri Permana Nabilla Yasmin Nurhadi Nurhadi Sandra Yulihartati Sarjon Defit Sovia, Rini Tsalsabila Jilhan Haura Yuhandri Yuhandri, Yuhandri