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All Journal Syntax Jurnal Informatika Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) bit-Tech Systematics Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Buana Ilmu Jurnal Tekinkom (Teknik Informasi dan Komputer) JISA (Jurnal Informatika dan Sains) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Bulletin of Computer Science Research J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks)
Mudzakir, Tohirin Al
Universitas Buana Perjuangan Karawang
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Implementasi Algoritma Support Vector Machine (SVM) dan Random Forest Untuk Klasifikasi Penyakit Hipertensi Berdasarkan Data Kesehatan Azhaar, Siti Alia; Mudzakir, Tohirin Al; Novita, Hilda Yulia; Faisal, Sutan
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8744

Abstract

One of the most common non-communicable diseases causing death in Indonesia is hypertension. At one community health center, the prevalence of hypertension is quite high. Based on examination results, more than 1,000 patients are diagnosed with hypertension each year. The issue faced at this health center is the lack of structured data classification for hypertensive and normal patients. The objective of this study is to compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms in creating a hypertension classification model based on health examination data from the Anggadita Health Center. Data from 2,500 patients was collected and preprocessed, including handling missing values, removing duplicate data, transforming data using label encoding, and dividing the data into training and testing sets. The SVM method applied a Radial Basis Function (RBF) kernel, while the RF consisted of 100 decision trees. Evaluation was conducted using a confusion matrix to calculate accuracy, precision, recall, and F1-score. The results showed that the SVM method achieved an accuracy of 93%, precision of 0.96 (Normal) and 0.90 (Hypertension), and F1-scores of 0.94 and 0.92. Meanwhile, the RF model showed superior performance with an accuracy of 96%, precision of 0.97 (Normal) and 0.95 (Hypertension), and F1-scores of 0.97 and 0.95, respectively. Thus, the Random Forest algorithm performs better in classifying hypertension data and can be implemented as a tool to assist healthcare institutions in managing patient data.
Pengembangan Model Klasifikasi Jenis Pisang Menggunakan Convolutional Neural Network Dengan Arsitektur VGG16 Habibah, Nur Habibah; Mudzakir, Tohirin Al; Novita, Hilda Yulia; Fauzi, Ahmad
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 6 No. 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8616

Abstract

Indonesia memiliki kekayaan varietas pisang yang melimpah, namun permasalahan utama yang dihadapi adalah kesulitan dalam mengidentifikasi dan mengklasifikasikan jenis-jenis pisang secara akurat, terutama karena kemiripan visual antar varietas. Proses identifikasi secara manual dinilai kurang efisien dan rawan kesalahan, terutama dalam skala besar. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi untuk lima jenis pisang, yaitu pisang ambon, pisang kapas, pisang nangka, pisang siam, dan pisang tanduk, menggunakan metode CNN berbasis arsitektur VGG16. Dataset yang digunakan terdiri dari 634 gambar pisang yang diperoleh melalui kamera smartphone dan telah melalui proses augmentasi serta normalisasi untuk meningkatkan keragaman data. Model dilatih dengan parameter learning rate 0,0001 batch size 32, dan epoch sebanyak 50. Hasil pelatihan akurasi mencapai 99,60% dan akurasi validasi sebesar 98,48%. Hasil evaluasi performa menggunakan confusion matrix dan matrix klasifikasi presisi, recall, dan F1-score menunjukan model memiliki kemampuan yang baik dalam menglasifikasikan jenis pisang dengan tingkat akurasi yang tinggi.
Air Quality Classification Using Naive Bayes Algorithm With SMOTE Technique Based on ISPU Data Fadhilah, Alya Febriyanti; Juwita, Ayu Ratna; Wicaksana, Yusuf Eka; Mudzakir, Tohirin Al
JISA(Jurnal Informatika dan Sains) Vol 8, No 1 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i1.2181

Abstract

Air pollution in DKI Jakarta is an important issue and has a negative impact on public health. This study applies the naive Bayes algorithm to classify air quality, Utilizing the SMOTE technique effectively addresses the issue of data imbalance. The data analyzed came from air pollution index data from 2022 to 2024, taken from five air monitoring stations in Jakarta. The analysis process was carried out following the CRISP-DM stages, starting from understanding the problem to evaluating the model. The results showed that SMOTE succeeded in increasing prediction accuracy in fewer classes. Without SMOTE, the model accuracy reached 90% but appeared biased towards fewer classes, with a recall value of only 0.75 and a precision of 0.62. While SMOTE, the model accuracy became 88%, with a precision value of 0.86, recall 0.87, and f1-score 0.87, which showed more balanced results across classes.
Automatic Classification of Public Complaints Using Naive Bayes Rico Andrean Hardiansyah; Jamaludin Indra; Dwi Sulistya Kusumaningrum; Tohirin Al Mudzakir
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2992

Abstract

Public complaint services are essential for improving government service quality by providing a direct channel for citizens to report issues. In Karawang Regency, the Tanggap Karawang (TANGKAR) platform serves this function; however, the manual classification of complaints causes delays and potential misrouting, especially due to the highly imbalanced distribution of complaint categories. This study develops an automatic classification model for public complaints in eight categories economy, education, health, social, infrastructure, security, environment, and transportation by integrating Term Frequency–Inverse Document Frequency (TF–IDF), Multinomial Naive Bayes, and Synthetic Minority Oversampling Technique (SMOTE). This integration addresses domain-specific class imbalance challenges, combining the computational efficiency of Naive Bayes, the feature representation strength of TF–IDF, and the improved minority class recognition from SMOTE. A dataset of 800 complaint records from TANGKAR underwent preprocessing, including cleaning, case folding, normalization, tokenizing, stemming, and stopword removal. TF–IDF with unigram and bigram features was used for feature extraction, followed by classification under two scenarios: original unbalanced data and balanced data via SMOTE. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. The model achieved 85.09% accuracy without SMOTE and 83.40% with SMOTE, with notable improvement in detecting minority categories after balancing. Although overall accuracy slightly decreased, SMOTE enhanced equitable prediction across all categories. This approach advances current public complaint classification methods by adapting to the linguistic diversity and uneven category distribution in actual e-government data, supporting faster and more accurate decision-making in public complaint management systems.
Co-Authors Abdul Gapur Abu Bakar Adi Rizky Pratama Agung Susilo Yuda Irawan Ahmad Farhan El Ramdhani Amril Mutoi Siregar Ayu Ratna Juwita Azhaar, Siti Alia Butar Butar, Naomi Nova Meylica Dwi Sulistya Kusumaningrum Fadhilah, Alya Febriyanti Fadmadika, Fadilla Faisal, Sutan Farah Maulida, Oxana Farhan El Ramdhani, Ahmad Fauzi Ahmad Muda Fitri Nur Masruriyah, Anis Habibah, Nur Habibah Hanny Hikmayanti Handayani Hermawan, Slamet Heryana, Nono Iin Kurniawati Indra, Jamaludin Irawan, Agung Susilo Yuda Iskandar, Muhammad Irsyad Jordi, Muhammad Juwita, Ayu Ratna Maulana, Moch Sigit Rizky Muhammad Jordi Novita, Hilda Yulia Nugraha, Bagja Pratama, Adi Rizky Pratama, Irfan Nugraha Ramadhan, Rizky Agung Rico Andrean Hardiansyah Rizal Saepul Mahdi rizky pratama, adi Rohana, Tatang Saepul Mahdi, Rizal Septiany, Eva Senia Setiyo Eko Budiyono Sukmawati, Cici Emilia Tatang Rohana Wahiddin, Deden Wicaksana, Yusuf Eka Yana Cahyana Yulia Novita, Hilda