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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Computatio : Journal of Computer Science and Information Systems JURNAL MEDIA INFORMATIKA BUDIDARMA JURNAL REKAYASA TEKNOLOGI INFORMASI Pendas : Jurnah Ilmiah Pendidikan Dasar Jurnal Teknologi Sistem Informasi dan Aplikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Jurnal Mnemonic Jurnal Sains Komputer dan Teknologi Informasi Journal of Computer Networks, Architecture and High Performance Computing Jurnal Cahaya Mandalika NUSANTARA: Jurnal Pengabdian Kepada Masyarakat YASIN: Jurnal Pendidikan dan Sosial Budaya Safari : Jurnal Pengabdian Masyarakat Indonesia Jurnal Teknik Mesin, Industri, Elektro dan Informatika Journal of Empowerment and Community Service Kegiatan Positif : Jurnal Hasil Karya Pengabdian Masyarakat Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Jurnal Informatika: Jurnal Pengembangan IT Jurnal Pengabdian Masyarakat Nian Tana GEMBIRA (Pengabdian Kepada Masyarakat) Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Buffer Informatika Jurnal Abdimas UM Jambi INOVTEK Polbeng - Seri Informatika Journal of Information Technology KREATIF: Jurnal Pengabdian Masyarakat Nusantara Jurnal Abdimas Mahakam Jurnal Publikasi Teknik Informatika
Wawan Joko Pranoto
Universitas Muhammadiyah Kalimantan Timur
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Workshop Penggunaan Mendeley sebagai Sistem Manajemen Referensi kepada Mahasiswa Arbansyah Arbansyah; Abdul Rahim; Rofilde Hasudungan; Wawan Joko Pranoto
Jurnal Abdimas Mahakam Vol. 7 No. 01 (2023): Abdimas Mahakam
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/jam.v7i01.1980

Abstract

Aplikasi Mendeley sangat membantu mahasiswa menyusun proyek ilmiah seperti skripsi, tesis, dan tugas akhir. Mahasiswa dapat mengelola bahan bacaan yang dijadikan referensi atau acuan dalam karya ilmiahnya dengan Mendeley ini. Siswa dapat lebih mudah memasukkan kutipan dan secara otomatis membuat daftar referensi dengan aplikasi ini. Banyak mahasiswa tingkat akhir yang masih belum memahami fitur atau manfaat aplikasi Mendeley ini. Akibatnya, pelatihan manajemen referensi berbasis Mendeley dan kegiatan pengabdian masyarakat sangat dibutuhkan. Tujuan dari pelatihan ini adalah untuk memperluas wawasan siswa dan meningkatkan kemahiran mereka dengan aplikasi manajemen referensi Mendeley. Pelatihan ini diharapkan dapat membantu mahasiswa meningkatkan kualitas karya ilmiahnya dan memberikan solusi atas permasalahan yang muncul saat penulisan tugas akhir.
Hybrid PSO Feature Selection Correlation and Support Vector Machine Model for Heart Disease Detection Sarina Safitri; Taghfirul Azhima Yoga Siswa; Wawan Joko Pranoto
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2251

Abstract

Heart disease remains a major health problem worldwide. The World Health Organization (WHO) reports that in 2022, approximately 19.8 million people died from heart disease, highlighting the need for the implementation of an appropriate early detection model. This study proposes a hybrid SVM–PSO model with correlation-based feature selection, duplicate data handling, and a multi-metric fitness function to enhance classification performance. PSO is employed to optimize the C parameter and RBF kernel of SVM, producing a more robust and balanced model compared to existing approaches. This study uses a heart disease dataset consisting of 1,025 rows with 13 attributes and 1 target variable obtained from the Kaggle repository and republished on the Zenodo platform in 2024. The research stages include Pre-Processing, Standardization, Feature Selection based on Correlation, and evaluation using the 10-Fold Cross Validation technique with Accuracy, precision, recall, and F1-score metrics. The results show that Support Vector Machine (SVM) achieved an Accuracy of 82.80%, Precision of 79.31%, Recall of 91.70%, and an F1-score of 84.88%. After optimization using PSO, the performance improved to an accuracy of 84.46%, precision of 80.54%, recall of 92.72%, and an F1-score of 86.04%. The experimental results indicate performance improvements of 2.00% in accuracy, 1.55% in precision, 1.11% in recall, and 1.37% in F1-score after PSO optimization. These results prove that the applied hybrid approach successfully improved the ability to detect heart disease. Therefore, this study contributes by demonstrating that PSO-based hyperparameter optimization can effectively enhance SVM classification performance for heart disease detection. The proposed model also has practical implications as a decision support tool for early heart disease detection that can assist medical practitioners in improving diagnostic accuracy and supporting preventive treatment strategies.
Model Hybrid PSO, Feature Selection Correlation dan Logistic Regression untuk Deteksi Penyakit Jantung Hidayatullah, Muhammad Wahyu; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
INFOMATEK Vol 28 No 1 (2026): Juni 2026 (In Progress)
Publisher : Fakultas Teknik, Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/infomatek.v28i1.43123

Abstract

Penyakit jantung merupakan salah satu penyebab utama kematian baik di Indonesia maupun secara global sehingga diperlukan model deteksi dini yang akurat. Penelitian ini bertujuan meningkatkan kinerja Logistic Regression dengan regularisasi L2 melalui optimasi Particle Swarm Optimization (PSO) dan feature selection berbasis correlation. Metode yang digunakan meliputi pre-processing, standarisasi, seleksi fitur, serta evaluasi menggunakan K-10 Fold Cross Validation. Hasil pengujian menunjukkan bahwa Logistic Regression menghasilkan accuracy 82,47%, precision 80,31%, recall 88,56%, dan F1-score 84,10%. Setelah dioptimasi dengan PSO, performa meningkat menjadi accuracy 84,45%, precision 81,74%, recall 91,01%, dan F1-score 85,98%. Hasil tersebut menegaskan bahwa pendekatan hybrid yang diusulkan efektif dalam meningkatkan deteksi penyakit jantung.
Penerapan Metode Forward Selection dan ADASYN Pada Algoritma SVM Untuk Klasifikasi Data Kecelakaan Lalu Lintas Muhammad Fadly Ramadhani; Taghfirul Azhima Yoga Siswa; Wawan Joko Pranoto
Buffer Informatika Vol. 11 No. 2 (2025): Buffer Informatika
Publisher : Department of Informatics Engineering, Faculty of Computer Science, University of Kuningan, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/buffer.v11i2.472

Abstract

Di Indonesia, khususnya kota Samarinda, angka kecelakaan lalu lintas menunjukkan tren peningkatan yang mengkhawatirkan. Jumlah kecelakaan meningkat dari 97 kasus pada tahun 2021 menjadi 102 kasus pada tahun 2022, dan mencapai 173 kasus pada tahun 2023. Penelitian ini bertujuan untuk meningkatkan akurasi klasifikasi tingkat kecelakaan lalu lintas dengan mengimplementasikan algoritma Support Vector Machine (SVM) yang dikombinasikan dengan metode seleksi fitur Forward Selection dan teknik oversampling ADASYN. Dataset yang digunakan merupakan data kecelakaan dari Polresta Samarinda periode 2020–2024 dengan 35 atribut, yang diseleksi menjadi 13 atribut relevan. Penelitian dilakukan melalui tahapan data pre-processing, balancing, pemodelan SVM, serta evaluasi menggunakan 10-fold cross-validation. Hasil pengujian menunjukkan bahwa penerapan ADASYN mampu meningkatkan akurasi model dari 70,75% menjadi 96,38%. Peningkatan lebih lanjut dicapai dengan Forward Selection, menghasilkan akurasi hingga 98,00%. Temuan ini membuktikan bahwa seleksi fitur dan penyeimbangan kelas memiliki kontribusi signifikan dalam memperkuat performa model klasifikasi SVM untuk analisis kecelakaan lalu lintas.
Penerapan Sistem Manajemen Rekam Web pada DPMPTSP Kota Samarinda dengan Menggunakan Framework Laravel Mohammad Hiqmal Fiqri; Wawan Joko Pranoto; Bayu Gaung Oktio Putra; Muhammad Nur Irvan; Wahyu Laksana
Jurnal Publikasi Teknik Informatika Vol. 3 No. 1 (2024): Januari: Jurnal Publikasi Teknik Informatika
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v3i1.2448

Abstract

The management of physical archives into digital formats is a crucial aspect in enhancing the operational efficiency of government agencies, particularly the DPMPTSP of Samarinda City. This research proposes and implements a web-based record management system using the Laravel framework to facilitate this process. The primary focus is to simplify employees' tasks in transforming and managing archives digitally, reducing dependence on physical archiving that often consumes time and resources. Laravel framework is chosen for its reliability in web development and ease of integration with other technologies.The implementation of this system not only transforms how employees store archives but also streamlines the file borrowing process. The system allows employees to easily search for and borrow archives electronically, overcoming traditional barriers in information retrieval. The implementation results show a significant improvement in archive management efficiency, creating an innovative and relevant solution to administrative challenges in government agencies. The success of this implementation creates opportunities to further modernize public administration processes, making technology utilization a key factor in improving productivity and service quality. Thus, this research makes a positive contribution to the transformation of public administration through technology implementation, paving the way for more effective and integrated archive management.
Model Hybrid Particle Swarm Optimization, Correlation Feature Selection dan Naive Bayes untuk Deteksi Penyakit Jantung Renaldi Yoga Rendy Menono; Taghfirul Azhima Yoga Siswa; Wawan Joko Pranoto
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10240

Abstract

Heart disease is a leading cause of mortality in Indonesia, with the number of cases reaching 15.5 million in 2022. This study aims to classify heart disease using the Naïve Bayes algorithm optimized with Particle Swarm Optimization (PSO) to improve classification performance. The dataset used in this study was obtained from the Zenodo repository, consisting of 1,025 heart disease records with 14 features. The data were processed through preprocessing stages and divided into training and testing sets using the 10-Fold Cross Validation method. PSO optimization was applied to the var_smoothing parameter of the Naïve Bayes algorithm. Model performance was evaluated using a confusion matrix to obtain accuracy, precision, recall, and F1-score values. The results indicate that Particle Swarm Optimization (PSO) improves the performance of the Naïve Bayes algorithm. On the correlation-based feature selection dataset, accuracy increased from 81.49% to 84.48%, precision slightly decreased from 82.57% to 82.37%, recall increased from 84.38% to 91.62%, and F1-score increased from 83.00% to 86.33%. These findings demonstrate that the combination of Naïve Bayes and Particle Swarm Optimization (PSO) is effective in enhancing heart disease classification performance.
Model Hybrid PSO-SMOTE, Correlation Feature Selection dan Random Forest untuk Deteksi Penyakit Jantung Nur Dila Yuanti; Taghfirul Azhima Yoga Siswa; Wawan Joko Pranoto
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10241

Abstract

Heart disease is one of the leading causes of death worldwide, with approximately 17.8 million deaths reported in 2021. In Indonesia, the number of cases reached an estimated 15.5 million in 2022, highlighting the need for accurate early detection. This study aims to improve heart disease classification by integrating Correlation Feature Selection (CFS), Synthetic Minority Oversampling Technique (SMOTE), Particle Swarm Optimization (PSO), and Random Forest. CFS was applied to remove less relevant features before classification, SMOTE addressed class imbalance, and PSO optimized the Random Forest hyperparameters. The dataset consisted of 1,025 records from four heart disease datasets, which were cleaned into 302 unique instances. CFS eliminated the fasting blood sugar (fbs) attribute due to its very weak correlation with the target variable. Evaluation using 10-fold cross-validation showed that the baseline Random Forest achieved an accuracy of 82.80%, precision of 83.62%, recall of 86.07%, and f1-score of 84.46%, while Random Forest–PSO with SMOTE produced the best performance, achieving 85.11% accuracy, 84.62% precision, 89.74% recall, and 86.77% f1-score. These findings indicate that PSO provides the greatest performance improvement, while CFS simplifies the feature set before classification. The proposed hybrid model contributes by integrating feature selection, data balancing, and hyperparameter optimization into a single classification framework for more effective heart disease prediction.
Analisis Kinerja Jaringan Wireless LAN dengan Menggunakan Metode QoS dan RMA pada SD Negeri 014 Sangasanga Achmad Maulidin; Wawan Joko Pranoto; Abdul Hallim
YASIN Vol 5 No 2 (2025): YASIN: Jurnal Pendidikan dan Sosial Budaya
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/yasin.v5i2.5232

Abstract

The quality of the wireless LAN network at SD Negeri 014 Sangasanga is suboptimal, particularly during the implementation of the Computer-Based National Assessment (ANBK). This condition leads to network overload and decreased performance, which disrupts the smooth execution of ANBK. This study aims to analyze network performance using Quality of Service (QoS) and Reliability, Maintainability, Availability (RMA) methods, focusing on throughput, packet loss, delay, and jitter parameters. Measurements were conducted using Wireshark and PRTG tools during ANBK sessions. The research method involves collecting network performance data under actual conditions. The results show that the throughput parameter is in the poor category, averaging 1,200 Kbps, with a peak value of 3,056 Kbps and a low of 10 Kbps. Packet loss demonstrated excellent performance with 0% recorded across all sessions. The average delay reached 46 ms, ranging from 2.7 ms to 430 ms, mostly meeting TIPHON standards. Jitter averaged 28.4 ms, with stable results despite occasional spikes in certain sessions. The findings highlight the need for better network management to support stable ANBK implementation. Recommendations include improving network infrastructure with more reliable devices and applying efficient bandwidth management techniques to ensure stable network performance during ANBK sessions.
Penerapan Metode K-Means Clustering Terhadap Bencana Kebakaran Di Kota Samarinda Wisnu Priyo Jatmiko; M. Gillang Ramadhani; M. Gilang Romadhon; Gilang Adhmadani; Rahmad Fardian; Wawan Joko Pranoto
Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Vol. 2 No. 1 (2024): Januari : Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupiter.v2i1.36

Abstract

Fire is a disaster that cannot be predicted when it will occur and where it will occur, it's just that densely populated areas are areas that are vulnerable to the danger of fire. Fire disaster in Samarinda City. Data obtained from the Samarinda City Fire and Rescue Service are fire incidents from 2021 to 2023. In 2021 there were 230 fire incidents, in 2022 there were 209 fires, in 2023 there were 99 fires. so this city is one of the cities that experiences the most fires on the island of Kalimantan. Several supporting facilities and infrastructure owned by the Samarinda City Fire and Rescue Department, such as hydrants and fire extinguishing posts, have been increased in number. This research functions to group fire data per year using the k-means clustering algorithm.
Metode Regresi Linier Berganda Untuk Prediksi Pemakaian Bbm Pt. Kalonica Bara Kusuma Augie Sugiarto Nunka; Wawan Joko Pranoto
Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Vol. 2 No. 1 (2024): Januari : Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupiter.v2i1.56

Abstract

PT. Kalonika Bara Kusuma is a company operating in the mining sector located in the city of Samarinda, East Kalimantan province. To achieve maximum profits, PT. Kalonika Bara Kusuma adds or subtracts units according to the amount of turnover obtained in the previous month. However, after being evaluated, it turned out that this method was not effective. Because you only see at a glance the fluctuations in historical data. Sometimes when you have reduced units, it turns out that demand in the following month actually increases. This results in less than optimal profits because they cannot serve existing customer requests. Vice versa. This is what causes PT. Kalonika Bara Kusuma experienced difficulty in making a decision to add or subtract units. From this problem, the author created an application that can predict the amount of turnover in the next month and provide recommendations for deciding which camera units should be increased or decreased in number. To predict the amount of turnover using the Multiple Linear Regression method. After obtaining the predicted results for the amount of turnover, a test was carried out using the Mean Absolute Percentage (MAPE) with a result of 200%, which means that the Multiple Linear Regression method is not suitable to be used to predict the amount of turnover in the next period. Production forecasting is a form of decision making that is used as a basis in many manufacturing and service industries. Therefore, companies that are able to produce products on time and in the right quantities are companies that are able to survive the competition. This demand forecasting is used to forecast demand for products that are independent (not dependent), such as forecasting finished products. The multiple linear regression method is an analytical technique that tries to explain the relationship between two or more variables, especially between variables that contain cause and effect, called regression analysis. So in relation to the description above, this research aims to determine production forecasting using the multiple linear regression method at PT. Kalonica Bara Kusuma.The mining industry is a series of activities that have a long period of time and costs a lot of money, a series of industrial activities, namely mining activities which include digging, loading and hauling to obtain optimal profits from activities. One of the mining industries needs to be a study of operational costs for transportation equipment
Co-Authors A Arbansyah A Halim Abdul Hallim Abdul Rahim Achmad Maulidin Agus Widodo AGUS WIDODO Alam, Aksal Illal Al Any Sawheri Gading Arbansyah Arbansyah Arif Nur Rahman Augie Sugiarto Nunka Aulia Khofifah Syamsuri Bayu Gaung Oktio Putra Damari, Azwar Della Eliyana Saputri Dinda Nur Octaviany Dini Anitasari Evitasari, Yuliana Dilla Faldi Faldi Faldi, Faldi Fitri Damayanti Fitriayana, Fitriayana Gilang Adhmadani Gina Maulidina Gunawan Ariyanto Hallim, Abdul Hasudungan, Rofilde Hidayatullah, Muhammad Wahyu Highness Mailani Putri Highness Mailani Putri Husni Thamrin Ibnu Sabdaniansyah Ika Safitri Windiarti Ilham, Muhammad Fauzan Nur Indra Pradista Indra Pradista Irma Yuliana Istimaroh Istimaroh Lidya Sari M. Gilang Romadhon M. Gillang Ramadhani Masni Masni Mawaddah, Suci Melisa Nur Aini Miliani, Dwi Fitri Mohammad Hiqmal Fiqri Mubaraq, Ahmad Ridhani Muhammad Fadly Ramadhani Muhammad Fath Thoriq Muhammad Nur Irvan Muhammad Rifqi Pratama MUTHMAINNAH Naufal Azmi Verdikha Novia Hidayati Ramadhani Nur Dila Yuanti Nurdin, Andi Pambudi, Faldy Alfareza Rahmad Fardian Ramadhan, Ahmad Kasim Ramadhan, Muhammad Firdaus Ramadhani, Daib Jidan Renaldi Yoga Rendy Menono Restu, Anggiq Karisma Aji Reyka Luna Karalo Reza, Andi Rida Priyanti Ridha Anisa Soldzu Parnga Ridha Anisa Soldzu Parnga Rita Yulfani Rivaldo, Vito Junivan Rofilde Hasudungan Sari, Septa Intan Permata Sarina Safitri Satria, Bima Siti Muawwanah Sofie Azizah, Jahra Syandy Apriyan Nur Taghfirul Azhima Yoga Siswa Taufiq, Ilham Tri Duwi Pramudito Wahyu Laksana Wahyudi Yulyanto Wisnu Priyo Jatmiko Yaakub, Saleh Yastria, Nurul Marisya