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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) 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 JURNAL ILMIAH INFORMATIKA 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 JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Intelligent Decision Support System (IDSS) Jurnal Sains Komputer dan Teknologi Informasi Didaktik : Jurnal Ilmiah PGSD STKIP Subang Journal of Computer Networks, Architecture and High Performance Computing Teknika Jurnal Cahaya Mandalika Jurnal Mandiri IT NUSANTARA: Jurnal Pengabdian Kepada Masyarakat YASIN: Jurnal Pendidikan dan Sosial Budaya Jurnal Pengabdian Masyarakat Nusantara 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 Insan Pengabdian Indonesia 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, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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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.
Implementation of the PSO-SMOTE Method on the Naive Bayes Algorithm to Address Class Imbalance in Landslide Disaster Data Azwar Damari; Taghfirul Azhima Yoga Siswa; Wawan Joko Pranoto
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/7wcvrb72

Abstract

Landslides in Samarinda, which often occur after floods, pose a threat to settlements, infrastructure, and the agricultural sector. This study proposes a combination of Naïve Bayes, SMOTE (Synthetic Minority Oversampling Technique), and PSO (Particle Swarm Optimization) to address class imbalance in landslide prediction. The results show that while PSO successfully improves the accuracy of the Naïve Bayes model, the application of SMOTE led to a decrease in accuracy for some method combinations. This decrease is due to changes in data distribution caused by synthetic data, which can introduce noise and affect feature selection and model optimisation. However, the combination of Naïve Bayes with PSO optimisation resulted in a modest accuracy improvement (+0.48%). These findings suggest that SMOTE should be used cautiously, while PSO is more effective in enhancing the accuracy of the landslide prediction model. The implications for practical application are that although SMOTE and PSO can improve accuracy, the impact of synthetic data on data distribution must be considered, and further testing is needed to ensure its effectiveness in real-world conditions.
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
Implementasi Metode Regresi Linear Dalam Prediksi Harga Cabai Keriting Di Kota Samarinda Lidya Sari; Novia Hidayati Ramadhani; Reyka Luna Karalo; Wawan Joko Pranoto
SABER : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Vol. 2 No. 1 (2024): Januari : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/saber.v2i1.682

Abstract

Chili is a popular vegetable in Indonesia, often used as a spice in various local dishes. The surge in demand before major celebrations, coupled with unpredictable weather, can impact chili production and lead to price fluctuations. Predicting prices becomes crucial to anticipate market changes and maintain economic stability in Indonesia. This study aims to predict the prices of curly red chili in Samarinda City in 2024 using the Linear Regression method. The data, sourced from the last three years (January 2021 to November 2023) via Lamin Etam's website, underwent processing with RapidMiner. Analysis using Root Mean Squared Error (RMSE) indicates an accuracy level of 240.487+/-, signifying a relatively large margin of error. These results underscore the importance of adding data attributes to enhance the accuracy of curly red chili price predictions in Samarinda City.
Clustering Penggunaan Fuel Pada PT Trasindo Murni Perkasa Menggunakan Algoritma K-Means Dini Anitasari; Wawan Joko Pranoto
Jurnal Teknik Mesin, Industri, Elektro dan Informatika Vol. 3 No. 1 (2024): Maret : JURNAL TEKNIK MESIN, INDUSTRI, ELEKTRO DAN INFORMATIKA
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jtmei.v3i1.3211

Abstract

In coal mining, vehicles are used that have a large physical shape so that they can carry quite a lot of material so that it doesn't take a long time to mine. A lot of fuel is used during mining. Mining units have an identification mark in the form of a unit number as the vehicle's identity. attached to the vehicle and this unit number will later be recorded in fuel usage. Due to the large amount of fuel consumed by each unit, a lot of data is collected and needs to be processed. In this study, researchers used the clustering method with the k-means algorithm. -means Clustering begins by determining the number of clusters. This research uses two clusters. The results obtained from this grouping are, units in the day shift have a higher consumption amount, namely 217 liters per unit, while units in the shift have a total consumption of 147 liters. per unit with an ARI value of 0.5399218293207123 and an NMI value of 0.6565191143081124.
Co-Authors A Arbansyah A Halim Abdul Hallim Abdul Hallim Abdul Rahim Abdul Rahim Achmad Maulidin Agus Widodo AGUS WIDODO Al Munawaroh Alam, Aksal Illal Al Any Sawheri Gading Arbansyah Arbansyah Ari Ahmad Dhani Arif Nur Rahman Asslia Johar Latipah Augie Sugiarto Nunka Aulia Khofifah Syamsuri Azwar Damari Bayu Gaung Oktio Putra Damari, Azwar Della Eliyana Saputri Denny Rachman Dinda Nur Octaviany Dini Anitasari Evitasari, Yuliana Dilla Fadzlul Rahman, Ferry Faldi Faldi Faldi Faldi Faldi Faldi Faldi, Faldi Farid Azis, Muhammad Fathur Rahman Fitri Damayanti Fitriayana, Fitriayana Gilang Adhmadani Gina Maulidina Gunawan Ariyanto Hafizh Mas'Ud, Muhammad Hallim, Abdul Harits, Sayekti Hasudungan, Rofilde Hendra Saputra 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 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 Bagus Indriarto Muhammad Fadly Ramadhani Muhammad Farid Azis Muhammad Fath Thoriq Muhammad Nur Irvan Muhammad Rifqi Pratama MUTHMAINNAH Naufal Azmi Verdikha Novia Hidayati Ramadhani Nur Dila Yuanti Nur Rahman, Rohim Nurdin, Andi Pambudi, Faldy Alfareza Raenald Syaputra Rahmad Fardian Ramadhan, Ahmad Kasim Ramadhan, Muhammad Firdaus Ramadhani, Daib Jidan Rangga Pranata Renaldi Yoga Rendy Menono Reno Ridhoi 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 Sayekti Harits Suryawan Siti Muawwanah Sofie Azizah, Jahra Syandy Apriyan Nur Taghfirul Azhima Yoga Siswa Taufiq, Ilham Tri Duwi Pramudito Tri Duwi Pramudito Trisha NurHalisha Wahyu Hidayat Wahyu Laksana Wahyudi Yulyanto Wisnu Priyo Jatmiko Yaakub, Saleh Yastria, Nurul Marisya Zahra, Amalia