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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Symmetry: Pasundan Journal of Research in Mathematics Learning and Education JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Didaktik : Jurnal Ilmiah PGSD STKIP Subang JOINT (Journal of Information Technology Jurnal Teknologi Informasi dan Komunikasi Bulletin of Computer Science Research International Journal of Management Science and Information Technology (IJMSIT) Abdi Cendekia: Jurnal Pengabdian Masyarakat Global Jurnal Manajemen Sistem Informasi Journal of Informatics, Electrical and Electronics Engineering Time in Physics: Journal for Theoretical, Instrumentation, Material-Molecular, and Education Physics Bulletin of Artificial Intelligence JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)

Timbo Faritcan Parlaungan Siallagan

Universitas Mandiri

Author-ID : 546134

Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Pencarian Nasabah dengan Menggunakan Data Mining dan Algoritma C4.5 Koperasi MADUMA Subang Timbo Faritcan Siallagan
Jurnal Teknik Informatika dan Sistem Informasi Vol 1 No 3 (2015): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v1i3.591

Abstract - Credit is the provision of money or bills that can be equated with it, based on consent or agreement between the bank and the borrowing other party require that borrowers pay off its debt after a certain period of time by the giving of flowers. Although the Lender has approved a credit proposed by the debtor, but the credit analysis to be done of debtors who have been approved so that the cause of non-performing loans can be examined and get a good classification for the determination of the appropriateness of granting credit. In granting credit need to analyse the needs of creditors, then that must be known in advance is the principles that need to be ditegakan in the framework of granting credit. Things that need to be considered in granting credit to customers is the principle 6 C's Analysis. With these problems, then there is need for troubleshooting existing solutions, by making a decision support System. Thus this decision support System will be able to meet the expectations to be achieved. The algorithm C 4.5 is algorithms used to create the decision tree. Decision tree classification method and prediction is a very powerful and famous. Getting rich in information or knowledge that is conceived by training data, the accuracy of the decision tree will be increased.Keywords - credit Analysis, principles 6 C's Analysis, Data Mining and algorithms C4.5

Perbandingan Kinerja Algoritma Naïve Bayes dan C4.5 pada Sistem Web Klasifikasi Kelayakan PKH Jupriyanto, Jupriyanto; Apandi, Jamaludin; Wijaya, Anderias Eko; Hermawan, Rian; Siallagan, Timbo Faritcan Parlaungan; Udoyono, Kodar; Ahmad, Hermansyah Nur
Jurnal Teknologi Informasi dan Komunikasi Vol 18 No 1 (2025): April
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/jtik.v18i1.287

This study discusses the development of a web-based classification system for determining the eligibility of recipients of the Family Hope Program (PKH), by comparing two data mining algorithms: C4.5 and Naïve Bayes. The dataset used includes various attributes relevant to eligibility assessment for social assistance. The C4.5 algorithm is employed to generate an interpretable decision tree, while the Naïve Bayes algorithm is used for probabilistic classification. The results show that Naïve Bayes achieved the highest accuracy at 98%, excelling in processing large datasets more efficiently. Meanwhile, C4.5 achieved an accuracy of 93.33% and offered better interpretability through its decision tree visualization. Both algorithms proved effective in classifying PKH eligibility and can be implemented in social assistance information systems to improve the accuracy and efficiency of the beneficiary selection process. This research concludes that the choice of algorithm should be based on system priorities—whether the focus is on processing speed or result interpretability.

Sistem Informasi Sistem Perangkap Hama Tikus Di Kandang Ayam Berbasis Iot Menggunakan Metode C.45 faritcan, timbo; Faritcan Siallagan, Timbo; Andrian, Avit
Jurnal Manajemen Sistem Informasi (JMASIF) Vol. 1 No. 2 (2022): Oktober 2022
Publisher : Divisi Riset, Lembaga Mitra Solusi Teknologi Informasi (L-MSTI)

Mice are animals that can interfere in people's lives. The nature of rats that always make runways, are very sensitive to bait, or are very familiar with their environment. When humans are sometimes negligent, that's when rats can change the existing environmental conditions according to their habitat. Rats really like to live in areas that are rarely touched by humans, such as in dark, damp, dirty, hidden places, near food sources and difficult to reach by humans. These animals are sometimes disturbing. After conducting this research authors found that this tool is able to reduce pests. rats around the chicken coop by means of automatic traps The research was conducted using the C4.5 Algorithm calculation method in which this method is used to perform calculations in order to determine decisions where parameters are calculated in order to see how many rats enter the trap. With this system, it can reduce rats that roam the house or in buildings where archives are stored which can interfere with people's lives. The object of this research is made in a chicken coop where you can see the number of rats that interfere with chicken hatching. For this reason, this research system is designed to reduce rat pests in chicken coops by using traps and will result in a reduction of rats in chicken coops and become a tool that people can use to deal with rat infestations.

Pelatihan Penggunaan Microsoft Office/Canva dalam Mendukung Pelayanan Prima pada Kader Posyandu Huda , Agnia Nurul; Untari, Dewi; Yanti, Yanti; Siallagan, Timbo Faritcan Parlaungan
Abdi Cendekia : Jurnal Pengabdian Masyarakat Vol 4 No 3 (2025): September
Publisher : Yayasan Zia Salsabila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61253/abdicendekia.v4i3.365

Pelayanan yang optimal di Posyandu sangat bergantung pada kapasitas kader untuk menyampaikan informasi yang jelas, menarik, dan terdokumentasi dengan baik. Namun demikian, di RW 13 Gading Tutuka 1, Soreang, Kabupaten Bandung, banyak kader Posyandu yang masih belum mengenal teknologi digital fundamental, seperti Microsoft Office dan Canva. Hal ini berdampak pada penyusunan laporan kegiatan yang tidak memadai, pelestarian data tentang balita dan ibu hamil, serta penyuluhan masyarakat yang belum disampaikan secara kasat mata dan informatif. Program ini berupaya meningkatkan kecakapan kader Posyandu dalam memanfaatkan Microsoft Word untuk pembuatan laporan, Excel untuk dokumentasi data, PowerPoint untuk presentasi penyuluhan, dan Canva untuk perancangan materi pembelajaran dan inisiatif promosi. Metode pelatihan dilaksanakan secara bertahap dan mencakup latihan praktik, sesuai dengan kebutuhan dan keterampilan peserta. Tujuannya agar kader Posyandu dapat meningkatkan kapasitasnya dalam memberikan pelayanan kesehatan masyarakat berbasis digital dan meningkatkan dokumentasi dan publikasi kegiatan Posyandu secara mandiri. Pelatihan ini merupakan tahap awal dalam membangun posyandu yang responsif terhadap kemajuan teknologi dan memberikan layanan berbasis informasi yang unggul.

CNN MODEL OPTIMIZATION USING MULTI-STAGE DATA AUGMENTATION FOR LOCAL PLANT LEAF DISEASE CLASSIFICATION Verdi Yasin; Timbo Faritcan P. Siallagan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7845

Plant leaf diseases are a major factor in reducing agricultural productivity, particularly for local commodities that often lack adequate artificial intelligence-based disease detection systems. This study aims to optimize the performance of a Convolutional Neural Network (CNN) model using the Inception V3 architecture through the application of multi-stage data augmentation to improve the classification accuracy of local plant leaf diseases. The dataset used is PlantifyDR from Kaggle, which has limited data volume and visual variation, requiring an effective augmentation strategy to improve the model's generalization ability. The proposed multi-stage augmentation approach consists of three stages—geometric, photometric, and texture-noise augmentation—that systematically enrich the diversity of training images. Evaluation results show that the proposed model provides significant performance improvements compared to the baseline model. The Inception V3 model with multi-stage augmentation achieved an accuracy of 0.762, an F1-score of 0.727, and a perfect AUC (1.00) across all classes, while the baseline model only achieved an accuracy of 0.595 and an average AUC of 0.877. Accuracy, loss, ROC curve, and confusion matrix analyses confirmed that multi-stage augmentation reduced overfitting and enhanced the model's ability to differentiate disease symptoms across leaf types. Therefore, this study concludes that multi-stage data augmentation is an effective approach for optimizing deep learning models on small and complex datasets, while also providing a significant contribution to the development of more accurate and reliable AI-based plant disease detection systems.

Garbage Disposal Information System Using Internet of Things Timbo Faritcan Siallagan
International Journal of Management Science and Information Technology Vol. 3 No. 1 (2023): January - June 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v3i1.923

The waste management carried out by the Puri Panji Kencana Residential and Buana Grand Subang Housing Communities, West Java, is related to the community's perception of waste and the condition of the area where they live. Therefore, this study aims to determine the public's perception of waste, the waste management system, and the public's perception of the effectiveness of waste management in areas with different topography. This study used a survey method with data collection techniques in the form of distributing questionnaires to the respondents. The sampling technique used in this study was quota sampling by selecting 90 respondents in the three research areas. The analysis technique used is the crosstabs and chi square techniques obtained using the SPSS program. The results of this study indicate that people in the three regions with different topography have a positive perception of waste and regard waste as items that can still be recycled. There are differences in waste management in the three research locations, the flatter an area, the better the level of waste management. The effectiveness of waste management according to public perceptions from various aspects is more felt by people in flat and slightly wavy topography.

Improved Genetic Algorithm with Adaptive Operators and Elitism for Random Forest Feature Selection in Heart Disease Classification Rahma Dhea Safitri; Solikhun Solikhun; Timbo Faritcan P. Siallagan
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10372

Heart disease is one of the leading causes of mortality worldwide, and accurate prediction models are essential to support early diagnosis. However, conventional Random Forest classifiers generally utilize all available features, although not all features contribute equally to classification performance, resulting in unnecessary model complexity. This study proposes an Improved Genetic Algorithm (IGA) that extends the conventional Genetic Algorithm through elitism, adaptive crossover, and adaptive mutation operators to optimize feature selection for Random Forest-based heart disease classification. The proposed method was evaluated using the Cardiovascular Disease Dataset from Kaggle, which consisting of 1,000 records and 14 variables, where 12 predictor features were used for model development. The experimental procedure included data preprocessing, train-test splitting, class imbalance handling using SMOTE on the training set, feature normalization, Random Forest modeling, feature selection using the proposed IGA, and model evaluation. The proposed IGA selected six important features slope, chestpain, restingBP, restingelectro, oldpeak, and gender. The optimized Random Forest model achieved an accuracy of 99.50%, precision of 99.15%, recall of 100.00%, F1-score of 99.57%, and AUC-ROC of 99.90%. These findings indicate that feature selection can simplify the model without compromising classification performance, making the Random Forest + IGA approach a viable alternative for developing more efficient heart disease prediction models.

Klasifikasi Penyakit Ginjal Kronis pada Data Tidak Seimbang Menggunakan K-Nearest Neighbor Berbasis Seleksi Fitur Mutual Information dan GridSearchCV Mirza Afif Pradivta; Solikhun Solikhun; Timbo Faritcan P Siallagan
Bulletin of Artificial Intelligence Vol 5 No 1 (2026): April 2026
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/buai.v5i1.227

Chronic Kidney Disease (CKD) is a progressive disease characterized by a gradual decline in kidney function and requires early detection to reduce the risk of severe complications. Machine learning has been widely applied to support CKD classification based on clinical attributes; however, medical datasets often contain missing values, a combination of numerical and categorical features, and class imbalance. This study aims to evaluate the performance of the K-Nearest Neighbor (KNN) algorithm for CKD classification using Mutual Information feature selection and GridSearchCV. The dataset consisted of 400 samples, including 250 CKD cases and 150 non-CKD cases. The proposed methodology included data cleaning, missing value imputation, categorical feature encoding, numerical feature normalization using MinMaxScaler, feature selection using SelectKBest with Mutual Information, and hyperparameter tuning using GridSearchCV. Model performance was evaluated using hold-out testing and 10-fold cross-validation. The hold-out evaluation showed that the KNN model with GridSearchCV achieved 100.00% accuracy, precision, recall, F1-score, and AUC on the test set. To ensure that this result was not dependent on a single train-test split, additional evaluation was conducted using 10-fold cross-validation. The cross-validation results yielded an average accuracy of 99.25% for the KNN model with GridSearchCV, indicating consistent performance across different data partitions. Meanwhile, the KNN model with Mutual Information feature selection and GridSearchCV achieved 98.75% accuracy, 100.00% recall, and a 99.01% F1-score, demonstrating competitive performance while using a more compact feature subset. The findings indicate that the application of GridSearchCV improved the performance of the KNN model on the dataset used, while Mutual Information contributed to selecting relevant features, enabling the model to maintain strong classification performance with a reduced number of features

Improved Convulational Neural Network dengan Transfer Learning dan Hyperparameter Tuning untuk peningkatan akurasi klasifikasi Citra Kanker Kulit Ega Wahyu Andani; Solikhun Solikhun; Timbo Faritcan P. Siallagan
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.v6i4.1217

Skin cancer is one of the diseases that requires early detection to increase the likelihood of successful treatment. The use of artificial intelligence, particularly Deep Learning, has become an effective alternative in assisting the automatic classification of skin cancer images. However, the high class imbalance and visual similarity between lesion types in skin cancer datasets remain challenges in achieving optimal classification performance. This study aims to improve the accuracy of skin cancer image classification using an Improved Convolutional Neural Network based on Transfer Learning and Hyperparameter Tuning. The dataset used is HAM10000, consisting of 10,015 dermoscopy images across seven diagnostic classes. The architecture employed is MobileNetV2 as a feature extractor combined with a custom classification head. The training process was carried out using a two-phase transfer learning strategy, namely the backbone freezing phase and the fine-tuning phase. To address class imbalance, class weighting and data augmentation were applied, while model optimization was performed using grid search over the parameters of learning rate, dense layer size, and dropout rate. Model performance was evaluated using accuracy, precision, recall, F1-score, and Area Under Curve (AUC) metrics. The results show that the proposed model achieved a test accuracy of 85.50%, a validation accuracy of 84.75%, a macro F1-score of 83.14%, and a mean AUC of 0.94. These results indicate that the combination of two-phase Transfer Learning and Hyperparameter Tuning is capable of improving the performance of MobileNetV2 in skin cancer image classification. The contribution of this research is the development of a classification model that achieves high accuracy, is computationally efficient, and is capable of handling class imbalance in the HAM10000 dataset.

Co-Authors Agnia Nurul Huda Agus Sudrajat Ahmad, Hermansyah Nur Alghifari, Muhammad Anderias Eko Wijaya Andrian, Avit Anestasya S, Sellyna Apandi, Jamaludin Asep Galih Miftah F. Dede Wisnu Dewi Untari Diah Lestari Dimas Juliana Tri N Ega Wahyu Andani Eka Susanti Endang Paza Pebrianti Eva Novia Triani Faelasivah, Fiky Fajar Fajar Ika Sriyanti Imin - Tugimin Iqbal Robiyana Jaja Jaja Juni Ismail Jupriyanto . Kodar Udoyono, Kodar Milawati, Fina Mirza Afif Pradivta Muhamad Agung Suhendra Muhamad Firmansyah Nita Wijaya Novianti, Wieke Nurul Utami Sudibyo Permana, Eka Rahma Dhea Safitri Regita Audyna Siregar Rian Hermawan Riki Winanjaya Sidik Abdul Miftah Siti Basrah Basrah SITI NURJANAH Solikhun Solikhun Solikhun Solikhun Suhendra, Muhamad Agung Tedi Sumardi Tita Tita Verdi Yasin Widiyanto Hermawan Wijaya, Handri Yanti Yanti

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