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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 Pengabdian Masyarakat Nian Tana GEMBIRA (Pengabdian Kepada Masyarakat) Journal of Computer-based Instructional Media Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Jurnal Abdimas UM Jambi INOVTEK Polbeng - Seri Informatika Journal of Information Technology KREATIF: Jurnal Pengabdian Masyarakat Nusantara Jurnal Abdimas Mahakam
Wawan Joko Pranoto
Universitas Muhammadiyah Kalimantan Timur
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Optimasi Random Forest dengan Genetic Algorithm dan Recursive Feature Elimination pada High Dimensional Data Stunting Samarinda Satria, Bima; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7883

Abstract

Stunting is a chronic malnutrition problem that disrupts children's growth, with long-term impacts on physical growth, cognitive development, and productivity in adulthood. In Indonesia, the prevalence of stunting is still above the WHO threshold, reaching 24.4% according to the 2021 Indonesian Nutritional Status Study (SSGI), and in Samarinda City, the prevalence reached 24.7% in 2021 with 1,402 toddlers identified as stunted. Addressing this problem requires a more structured data-driven approach to provide targeted interventions. This study uses data from the Samarinda City Health Office, encompassing 150,474 stunting data points, and involves data collection, data cleaning, feature selection, and classification model application. This study aims to improve the accuracy of stunting data classification in Samarinda City in 2023 using the Random Forest algorithm enhanced with Recursive Feature Elimination (RFE) feature selection techniques and Genetic Algorithm (GA) optimization. The feature selection results using RFE show that the most influential features are Weight, ZS TB/U, ZS BB/U, and BB/U. The application of RFE increased the model's average accuracy from 91.91% to 93.64%, while GA optimization further increased the average accuracy to 98.39%. The definite accuracy increased from 94.23% (baseline model) to 97.10% (with RFE) and reached 99.70% (with RFE and GA). The combination of RFE and GA has proven effective in tackling data complexity and improving the reliability of stunting predictions. This study significantly contributes to the development of machine learning techniques for high-dimensional data analysis in health and is expected to be the foundation for more effective intervention programs in addressing stunting issues in Indonesia.
PENERAPAN METODE NAIVE BAYES KLASIFIKSI KELAYAKAN PENERIMA BANTUAN PANGAN NON TUNAI (BPNT) Sofie Azizah, Jahra; Pranoto, Wawan Joko; Hasudungan, Rofilde
Jurnal Mnemonic Vol 8 No 1 (2025): Mnemonic Vol. 8 No. 1
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v8i1.12778

Abstract

Program Bantuan Pangan Non Tunai (BPNT) masih menghadapi kendala dalam menentukan penerima yang benar-benar layak sehingga diperlukan metode klasifikasi yang dapat meningkatkan ketepatan dalam seleksi penerima bantuan. Penelitian ini bertujuan untuk mengklasifikasikan kelayakan penerima BPNT di Kelurahan Bukit Biru menggunakan metode Naïve Bayes. Data yang digunakan mencakup 1041 data kelayakan penerima BPNT yang diperoleh dari Kelurahan Bukit Biru pada tahun 2023 dengan data yang mencakup jumlah penghasilan, jumlah tanggungan, jumlah kendaraan, status perkawinan, jenis pekerjaan, dan kondisi rumah. Model Naïve Bayes diterapkan dengan pembagian data latih dan data uji dengan rasio 9:1. Naïve Bayes bekerja dengan menghitung probabilitas setiap kelas berdasarkan atribut yang diberikan dan menentukan hasil akhir berdasarkan probabilitas tertinggi, menjadikannya metode yang efektif untuk klasifikasi data BPNT. Hasil penelitian menunjukan bahwa metode Naïve Bayes berhasil menentukan kelas kedalam dua kategori yaitu layak atau tidak layak dengan akurasi sebesar 90%. Oleh karena itu diharapkan penelitiaan ini dapat membantu meningkatkan ketepatan sasaran dalam penyaluran bantuan sosial. Dengan demikian, penelitian ini dapat berkontribusi dalam meningkatkan efisiensi program bantuan sosial dan mendukung pengentasan kemiskinan.
ANALISIS KUALITAS LAYANAN JARINGAN 4G LTE MENGGUNAKAN METODE WALK TEST DAN PENGUKURAN QOS (QUALITY OF SERVICE) DI SAMARINDA CENTRAL PLAZA Tri Duwi Pramudito; Wawan Joko Pranoto; Abdul Hallim
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 01 (2025): Volume 10, Nomor 01, Maret 2025
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i01.22570

Abstract

This study aims to analyze the quality of 4G LTE network services at Samarinda Central Plaza (SCP) using the Walk Test method and Quality of Service (QoS) measurements for three major providers: Telkomsel, XL Axiata, and Indosat. Measured parameters include Reference Signal Received Power (RSRP), Signal-to-Interference-plus-Noise Ratio (SINR), Reference Signal Received Quality (RSRQ) using the G-NetTrack Pro application, and throughput, delay, and packet loss using the Wireshark application. The results indicate that Telkomsel exhibited the most stable RSRP performance within the "Good" category. XL Axiata excelled in throughput and SINR, while Indosat demonstrated significant performance variations, particularly in SINR and throughput. Environmental factors such as visitor density, building materials, and the location of Base Transceiver Stations (BTS) affected network quality at SCP. All providers recorded low delay values and minimal packet loss, reflecting reliable networks overall. Based on the findings, it is recommended that Telkomsel enhance network capacity in high-traffic areas, XL Axiata expand its excellent signal coverage, and Indosat improve connection stability. Additionally, collaboration between service providers and SCP management is suggested for installing signal-boosting devices in indoor areas to ensure optimal network quality, especially in high-activity zones. This study provides significant contributions to improving mobile network service quality in crowded environments like SCP.
Wireless Network Quality Analysis Using RMA and RSSI Methods at BPKAD Berau District Mubaraq, Ahmad Ridhani; Pranoto, Wawan Joko; Hallim, Abdul
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5718

Abstract

Wireless networks are now essential in supporting government operations, including at the BPKAD office in the Berau district. However, problems like unstable connections and slow speeds often arise as obstacles. This study aims to evaluate the quality of the wireless network in the BPKAD asset room of the Berau district by applying the Reliability, Maintainability, and Availability (RMA) and Received Signal Strength Indication (RSSI). Quantitative research method. The research population is all wireless access points (Wi-Fi) spread across the BPKAD office. The research sample is the asset field room. Data collection methods through observation, RMA measurement, and RSSI measurement. The data that has been collected will be analyzed using the RMA (Reliability, Maintainability, and Availability) and RSSI (Received Signal Strength Indication) methods. The results obtained show that most of the measurement days recorded network availability (availability) of 100%. However, there was a decrease on August 26, 2024 (99.58%) and September 3, 2024 (97.05%) due to the increased frequency of system failures. The analysis of RSSI showed that the signal quality fell into the excellent category with an average of -36.6 dBm. However, a decrease was recorded on August 30, 2024, with a value of -44 dBm. The results of this study underscore the importance of regular maintenance and upgrades to the network infrastructure in anticipation of possible deterioration. Recommendations include improving security systems, hardware updates, and technical training for IT staff to strengthen the network's support of activities at the BPKAD Office of Berau Regency.
Halal Validation and Product Quality as Added Value for Risoles SMEs in Samarinda City: Validasi Kehalalan dan Kualitas Produk sebagai Nilai Tambah UMKM Risoles di Kota Samarinda Ilham, Muhammad Fauzan Nur; Pranoto, Wawan Joko; Reza, Andi; Nurdin, Andi; Alam, Aksal Illal Al; Halim, Abdul
Journal of Empowerment and Community Service (JECSR) Vol. 3 No. 1 (2023): November
Publisher : Wadah Inovasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53622/jecsr.v3i1.370

Abstract

This community service initiative addresses the critical need for halal certification and quality assurance in Risoles SMEs to enhance consumer trust and market competitiveness. The study employed a mixed-method approach, combining observational analysis of production processes, ingredient validation, and stakeholder interviews. Key findings reveal that rigorous halal validation of raw materials (e.g., mayonnaise, cheese, and frying oil) and hygiene-compliant production practices significantly improved consumer confidence. Post-intervention sales increased by 35%, attributed to transparent halal labeling and quality guarantees. The proposed solution includes standardized halal documentation frameworks and continuous quality control training for SMEs. These efforts align with Indonesia’s growing demand for halal-certified food products, offering a replicable model for similar SMEs.
APPLICATION OF K-NEAREST NEIGHBOUR, RECURSIVE ELIMINATION AND ADASYN ALGORITHMS ON DERMATITIS DISEASE CLASSIFICATION DATA Ramadhani, Daib Jidan; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6656

Abstract

Dermatitis is a common type of non-infectious skin disease frequently found in Indonesia. Its prevalence is influenced by several factors such as poor hygiene, environmental conditions, and climate change. Data from RSUD Jagakarsa recorded that from 1,066 skin disease cases between February 2023 and January 2024, approximately 62.2% were non-infectious, and 34.4% of those were classified as dermatitis. The diagnostic process for dermatitis is often challenging due to its symptom similarity with other skin conditions, leading to potential misclassification. Therefore, a more accurate and efficient classification approach is required to support medical professionals in identifying dermatitis cases effectively. This study proposes the use of a combination of machine learning methods: K-Nearest Neighbor (KNN) as the core classification algorithm, Recursive Feature Elimination (RFE) for feature selection, and Adaptive Synthetic Sampling (ADASYN) to handle class imbalance within the dataset. The data was sourced from UPTD Puskesmas Bontang Barat in 2024, consisting of 392 samples and 10 main features. Evaluation was conducted using a 10-fold cross-validation scheme. Results showed that the baseline KNN model achieved an average accuracy of 62.23%. With ADASYN applied, the accuracy improved to 63.56%, and further increased to 92.71% when combined with feature selection using RFE.
Optimizing library catalogue management using object-oriented e-catalogue application: A case study at Universitas Muhammadiyah Jambi Yaakub, Saleh; Windiarti, Ika Safitri; Pranoto, Wawan Joko
Journal of Computer-based Instructional Media Vol. 3 No. 2 (2025): Regular Issue
Publisher : Researcher and Lecturer Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58712/jcim.v3i2.145

Abstract

The development of a comprehensive and efficient library catalogue system is essential for improving accessibility to information in modern educational environments. This research addresses the inefficiencies of manual cataloguing methods at Universitas Muhammadiyah Jambi’s library by proposing an Object-Oriented Programming (OOP)-based E- catalogue system. The system, developed using Visual Basic and Microsoft Access, streamlines the book-searching process, enhancing user access to book details such as authors, titles, and synopses. The system’s user-friendly interface supports features like search, administration, and data backup, improving both search efficiency and user satisfaction. This research contributes to the advancement of library management systems, demonstrating the potential of OOP-based design for future applications in similar academic institutions. Future research can build on this work by integrating machine learning techniques for personalized book recommendations and expanding the system's scalability.
Optimasi SVM dengan RFE dan ROS untuk Mengatasi High Dimension dan Imbalanced Data Banjir Pambudi, Faldy Alfareza; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.41068

Abstract

Floods are natural disasters that often occur in Indonesia, one of which is the city of Samarinda which experienced a significant increase in flood cases in 2018-2021. The use of machine learning, especially the Support Vector Machine (SVM) algorithm, aims to accurately predict future flood events, but the main problem faced is data imbalance and high-dimensional data. This research combines SVM with Random Oversampling (ROS) oversampling techniques and Recursive Feature Elimination (RFE) feature selection to overcome data imbalance and high-dimensional data, with the aim of increasing the classification accuracy of Samarinda City flood data. The cross validation method is with 10-fold cross-validation, and the model performance is evaluated with a confusion matrix to calculate the accuracy value. The data used was obtained from BPDB and BMKG Samarinda City for the 2021-2023 period, consisting of 11 attributes and 1095 lines of data. The research results show that RFE succeeded in identifying the five most important features, namely minimum temperature (Tn), maximum temperature (Tx), average temperature (Tavg), humidity (RH_avg) and maximum wind direction (ddd_x). With the combination of SVM, ROS, and RFE models, flood data classification accuracy increased by 0.78% from 97.14% to 97.92%.
Model Optimasi SVM-GSBE dalam Menangani High Dimensional Data Stunting Kota Samarinda Siti Muawwanah; Taghfirul Azhima Yoga Siswa; Wawan Joko Pranoto
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.41545

Abstract

Stunting has become a widely discussed health issue in Indonesia, par-ticularly in Samarinda City, which recorded a prevalence of 12.7% in 2023, making it the highest in East Kalimantan Province. The use of data mining techniques becomes crucial in overcoming the challenges of high dimensional data, such as computational complexity, the risk of overfitting, and visualization difficulties. This study aims to enhance the accuracy of Support Vector Machine optimization models using Grid Search and Backward Elimination feature selection (SVM-GSBE) to handle high-dimensional data related to stunting in Samarinda City. The dataset used is sourced from Samarinda City Health Office in 2023, covering 26 community health centers with 21 attributes and a total of 150,466 records. The research methodology includes data collection, pre-processing, data partitioning using K-Fold Cross Validation, feature selection using Backward Elimination, and SVM model optimization with Grid Search. Features such as BB/U, ZS TB/U, ZS BB/U, ZS BB/TB, Height, and LiLA have proven to increase accuracy in stunting data classification. Evaluation results show that Grid Search successfully increased accuracy for Linear from 99.59% to 99.78%, Polynomial from 90.92% to 99.40%, RBF from 89.80% to 98.36%, and Sigmoid from 75.29% to 86.84%. This indicates that the SVM-GSBE model can effectively be used as a tool for early detection of stunting and to support health policies in Samarinda City.
Model Optimasi KNN-PSORF dalam Menangani High Dimensional Data Banjir Kota Samarinda Restu, Anggiq Karisma Aji; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.41587

Abstract

Floods are a natural phenomenon that frequently occurs in Indonesia, including in Samarinda City which has faced flood issues over the past three years, affecting thousands of homes and around 27,000 residents. Predicting flood disasters requires machine learning technology using data mining classification methods. However, classification processes often encounter issues related to high-dimensional data, which can lead to overfitting and class imbalance, thereby biasing dominant classes while neglecting minority classes. This research aims to enhance classification accuracy in Samarinda City's flood data using the K-Nearest Neighbor (KNN) algorithm combined with Relief feature selection and Particle Swarm Optimization (PSO) optimization. The validation method employed is 10-fold cross-validation, with performance evaluation using a confusion matrix. Data sourced from Samarinda City's Disaster Management Agency (BPBD) and Meteorology, Climatology, and Geophysics Agency (BMKG) spans from 2021 to 2023, comprising 19 features and a total of 1095 records. Relief feature selection identified four crucial features: maximum wind direction, wind speed, average wind speed, and maximum wind speed direction. Average evaluations with k values of 3, 5, 7, 11, 13, and 15 demonstrate that Relief feature selection and PSO optimization effectively enhance accuracy in the K-Nearest Neighbor algorithm for flood data, with KNN and PSO yielding improvements of 2-5%. Relief feature selection alone improves accuracy by 1-2%, while combining Relief with PSO provides a 2-5% enhancement. The combined KNN, Relief, PSO model is expected to deliver optimal performance in classifying Samarinda City's flood data.
Co-Authors A Arbansyah A Halim Abdul Hallim Abdul Rahim 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 Maulidin, Achmad Mawaddah, Suci Melisa Nur Aini Miliani, Dwi Fitri Mohammad Hiqmal Fiqri Mubaraq, Ahmad Ridhani Muhammad Fath Thoriq Muhammad Nur Irvan Muhammad Rifqi Pratama MUTHMAINNAH Naufal Azmi Verdikha Novia Hidayati Ramadhani Nurdin, Andi Pambudi, Faldy Alfareza Rahmad Fardian Ramadhan, Ahmad Kasim Ramadhan, Muhammad Firdaus Ramadhani, Daib Jidan 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