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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 Jurnal Teknologi Sistem Informasi dan Aplikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Abdimas Mahakam Jurnal Mnemonic Jurnal Sains Komputer dan Teknologi Informasi Journal of Computer Networks, Architecture and High Performance Computing NUSANTARA: Jurnal Pengabdian Kepada Masyarakat YASIN: Jurnal Pendidikan dan Sosial Budaya Safari : Jurnal Pengabdian Masyarakat Indonesia Jurnal Teknik Mesin, Industri, Elektro dan Informatika Jurnal Publikasi Teknik Informatika (JUPTI) 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) Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Wawan Joko Pranoto
Universitas Muhammadiyah Kalimantan Timur
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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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MODEL RFGS-CS UNTUK MENGATASI HIGH DIMENSIONAL DATA STUNTING KOTA SAMARINDA Sari, Lidya; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5997

Abstract

Di Samarinda, Kalimantan Timur, prevalensi stunting terus meningkat, dengan angka mencapai 23,9% pada tahun 2022. Kondisi ini menunjukkan perlunya intervensi lebih efektif untuk mengatasi masalah gizi di wilayah tersebut. Metode klasifikasi data mining dapat memprediksi risiko stunting, namun penelitian sebelumnya menghadapi tantangan  dengan dataset berdimensi tinggi yang dapat mempengaruhi akurasi. Tujuan dari penelitian ini adalah untuk meningkatkan akurasi klasifikasi stunting di Kota Samarinda menggunakan algoritma Random Forest (RF) yang dioptimalkan dengan seleksi fitur Chi-Square dan optimasi parameter Grid Search. Dataset yang digunakan adalah data stunting dari 26 puskesmas di Kota Samarinda tahun 2023 dari Dinas Kesehatan Kota Samarinda. Metode validasi yang digunakan yaitu cross-validation dengan k=10. Hasil penelitian menunjukkan bahwa fitur-fitur seperti BB/U, Tinggi, ZS BB/U, ZS TB/U adalah yang paling signifikan dalam mempengaruhi performa model RF. Model RF dengan seleksi fitur Chi-Square mencapai akurasi sebesar 99.11%, tidak ada peningkatan akurasi setelah penambahan metode optimasi Grid Search. Hasil penelitian ini menunjukkan bahwa model Random Forest (RF), baik dengan maupun tanpa optimasi, efektif dalam mengklasifikasikan data stunting. Keefektifan model ini dalam menangani dataset yang rumit dan kompleks, sehingga diharapkan dapat mendukung kebijakan serta intervensi kesehatan
Perbaikan Akurasi Naïve Bayes dengan Chi-Square dan SMOTE Dalam Mengatasi High Dimensional dan Imbalanced Data Banjir Rivaldo, Vito Junivan; 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.v8i3.7886

Abstract

Floods are one of the natural disasters that frequently occur in Indonesia. The city of Samarinda is affected by floods every year, resulting in significant losses. The data used in this study comes from the Regional Disaster Management Agency (BPBD) and the Meteorology, Climatology, and Geophysics Agency (BMKG) for the years 2021-2023 in Samarinda. This data includes 11 attributes and 1095 records. Previous studies on data mining related to floods have been conducted. However, issues arise with high-dimensional data and data imbalance. High dimensionality leads to overfitting and reduced accuracy, while imbalanced data causes overfitting to the majority class and inaccurate representation. This study aims to improve the accuracy of the Naive Bayes algorithm in predicting high-dimensional and imbalanced flood data. The approach involves using the Chi-Square feature selection technique and oversampling with the Synthetic Minority Over-sampling Technique (SMOTE). Chi-Square is used to find optimal features for predicting floods and to enhance the accuracy of the Naive Bayes algorithm in predicting high-dimensional and imbalanced flood data. The validation method used is 10-fold cross-validation, and a confusion matrix model is employed to calculate accuracy values. The results of the study show that Chi-Square can identify four best features: average humidity (rh_avg), rainfall (rr), maximum wind direction (ddd_x), and most frequent wind direction (ddd_car). The use of the Naive Bayes algorithm with SMOTE achieved an accuracy of 71.58%. However, after applying Chi-Square feature selection, the accuracy dropped to 60.82%. This decline is attributed to the reduced number of minority classes after feature selection. Therefore, Chi-Square feature selection is not sufficiently effective in improving the accuracy of Naive Bayes on high-dimensional data.
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 Kinerja Jaringan Wireless LAN dengan Menggunakan Metode QoS dan RMA pada SD Negeri 014 Sangasanga Maulidin, Achmad; Pranoto, Wawan Joko; Hallim, Abdul
YASIN Vol 5 No 2 (2025): APRIL
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.
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.
Co-Authors A Halim Abdul Hallim Abdul Rahim Agus Widodo Alam, Aksal Illal Al Anggiq Karisma Aji Restu Any Sawheri Gading Arbansyah Arbansyah Arif Nur Rahman Augie Sugiarto Nunka Aulia Khofifah Syamsuri Bayu Gaung Oktio Putra Della Eliyana Saputri Dinda Nur Octaviany Dini Anitasari Evitasari, Yuliana Dilla Faldi Faldi Faldi, Faldi Fitri Damayanti Gilang Adhmadani Gina Maulidina Gunawan Ariyanto Hallim, Abdul Hasudungan, Rofilde Highness Mailani Putri Highness Mailani Putri Husni Thamrin Ibnu Sabdaniansyah Ilham Taufiq Ilham, Muhammad Fauzan Nur Indra Pradista Indra Pradista Lidya Sari M. Gilang Romadhon M. Gillang Ramadhani Maulidin, Achmad Mawaddah, Suci Melisa Nur Aini Mohammad Hiqmal Fiqri Mubaraq, Ahmad Ridhani Muhammad Fath Thoriq Muhammad Nur Irvan Muhammad Rifqi Pratama Muthmainnah Naufal Azmi Verdikha Novia Hidayati Ramadhani Nurdin, Andi Rahmad Fardian Ramadhani, Daib Jidan Reyka Luna Karalo Reza, Andi Ridha Anisa Soldzu Parnga Ridha Anisa Soldzu Parnga Rita Yulfani Rivaldo, Vito Junivan Rofilde Hasudungan Sari, Septa Intan Permata Satria, Bima Siti Muawwanah Sofie Azizah, Jahra Syandy Apriyan Nur Tagfirul Azima Yoga Siswa, Muspidar Mulyadi Tri Duwi Pramudito Wahyu Laksana Wahyudi Yulyanto Wisnu Priyo Jatmiko Yuliana, Irma