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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Journal of Information System Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Seminar Nasional Teknologi Informasi Komunikasi dan Industri Al Qalam: Jurnal Ilmiah Keagamaan dan Kemasyarakatan Jurnal Telematika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal ICT : Information Communication & Technology Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Abdimas Indonesia : Jurnal Abdimas Indonesia Jurnal Pengabdian Kepada Masyarakat Patikala Jurnal Pengabdian Masyarakat Sultan Indonesia
Rushendra, Rushendra
Informatics Study Program, Faculty Of Computer Science, Universitas Mercu Buana, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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PREDIKSI PENYAKIT DIABETES BERDASARKAN PERBANDINGAN KLASIFIKASI METODE K-NEAREST NEIGHBOR, NAÏVE BAYES, DAN DECISION TREE MENGGUNAKAN RAPID MINER Ardianto, Muhammad Rezanur; Rushendra, Rushendra
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6079

Abstract

Pada era digital seperti saat ini kegiatan manusia dipermudah dengan adanya teknologi yang tak terkecuali dalam bidang penjualan makanan dan minuman, namun dengan kemudahan tersebut mengakibatkan kesulitan masyarakat dalam melihat gizi dari makanan dan minuman yang mengakibatkan terjangkitnya penyakit Diabetes, akan tetapi penyakit tersebut banyak faktor yang dapat memengaruhinya . Oleh sebab itu penelitian ini dilakukan sebuah prediksi terjangkitnya penyakit Diabetes dengan melakukan perbandingan algoritma K-NN, Naïve Bayes, dan Decision Tree. Hasil dari perbandingan algoritma yang paling cocok pada kondisi default yaitu Decision Tree dengan tingkat akurasi 93,60%, namun untuk menghindari overfitting dan underfitting perlu dilakukan optimasi K cross validation pada K=5 sampai K=10, kemudian dilakukan optimasi nilai Konstanta K pada K=10. algoritma K-NN dengan K=2, sehingga didapatkan hasil algoritma K-NN lebih cocok untuk prediksi penyakit diabetes dengan nilai akurasi 96.13%.
Penerapan Bahasa Pemrograman HTML Python sebagai perangkat pendukung dalam pelayanan Masyarakat Pada Tim PKK Kelurahan Duri Kepa Kebon Jeruk Jakarta Barat Mohamad Yusuf; Roy Mubarak; Rushendra Rushendra; Siti Maesaroh; Nungky Awang Candra
Jurnal Abdimas Indonesia Vol. 5 No. 1 (2025): Januari-Maret 2025
Publisher : Perkumpulan Dosen Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34697/jai.v5i1.1327

Abstract

Tim Pemberdayaan dan Kesejahteraan Keluarga (PKK) di Kecamatan Duri Kepa berperan penting dalam menyebarkan informasi dan mendukung pengambilan keputusan tentang kesehatan masyarakat. Dengan meningkatnya kebutuhan akan solusi berbasis web, pengetahuan tentang teknologi seperti HTML, CSS, dan Python menjadi semakin krusial. Teknologi ini memungkinkan pengembangan sistem informasi yang lebih interaktif dan efektif, bahkan untuk pemula. Untuk menghadapi tantangan ini, program pelatihan telah disiapkan untuk memberikan anggota PKK keterampilan yang diperlukan dalam pengembangan web. Pelatihan ini menerapkan metode pembelajaran interaktif dan langsung di laboratorium universitas, dengan penekanan pada praktik HTML dan Python. Metode ini memberikan kesempatan bagi peserta untuk menerapkan keterampilan yang diperoleh dalam proyek berbasis web yang relevan dengan tugas mereka di PKK. Hasil dari kegiatan ini menunjukkan bahwa pelatihan berlangsung sukses dan peserta menunjukkan antusiasme yang tinggi. Mereka merasa nyaman dalam mengikuti pelatihan dan mampu menggunakan pengetahuan tentang HTML dan Python untuk membuat aplikasi sederhana. Program ini diharapkan dapat meningkatkan efektivitas intervensi kesehatan di tingkat komunitas serta mendukung pengambilan keputusan yang berbasis data dan berkelanjutan dalam konteks kesehatan masyarakat.
WORKSHOP PENGENALAN TOOL DEEP LEARNING UNTUK KLASIFIKASI GAMBAR UNTUK MENENTUKAN STATUS SOSIAL DAN REKOMENDASI BANTUAN SOSIAL Yusuf, Mohamad; Hakim, Lukman; Rushendra; Awang, Nungky
Jurnal Pengabdian Kepada Masyarakat Patikala Vol. 4 No. 4 (2025): ABDIMAS PATIKALA
Publisher : Education and Talent Development Center of Indonesia (ETDC Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51574/patikala.v4i4.3355

Abstract

This Community Service Activity was carried out as an effort to improve operational efficiency in travel service business actors, especially PT Swabina Gatra Travel, through optimizing the use of the Bookswantastic and Jurnal ID digital systems. Although both systems have been implemented to support the ticket booking process and financial transaction recording, the lack of integration of these systems still causes obstacles such as data duplication, price input errors, and increased employee workload. Through this activity, the community service team provides education-based solutions and assistance in the form of training on the use of more effective digital systems, preparation of internal Standard Operating Procedures (SOPs), and socialization of the potential for system integration through API technology. It is hoped that this activity can improve the understanding and skills of business actors in managing digital-based business processes efficiently, while strengthening the readiness for digital transformation in the travel service sector.
Analysis of Sulawesi Earthquake Data from 2019 to 2023 using DBSCAN Clustering Wijaya, Ody Octora; Rushendra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5819

Abstract

Sulawesi is a region in Indonesia known for its significant seismic activity, and its history of impactful earthquakes makes it an area of crucial importance for in-depth analysis. This study analyses earthquake occurrence data in the Sulawesi region from 2019 to 2023 using clustering methods with the DBSCAN algorithm. The utilization of the DBSCAN algorithm was chosen for its ability to cluster data based on spatial density, well-suited for analyzing the spatial patterns of earthquakes. DBSCAN is known for its effectiveness in identifying spatial clusters, especially in handling data with undefined density patterns. The primary aim of this research is to identify spatial earthquake occurrence patterns, classify regions with similar earthquake occurrence rates, describe the characteristics of the resulting spatial clusters, and identify seismic gap areas. The results of analysis and clustering using the DBSCAN algorithm have identified clusters with earthquake depth characteristics, which are expected to make a significant contribution to mapping and understanding earthquake vulnerability and distribution in this region. These findings can aid in more effective disaster mitigation planning, support sustainable development efforts, and enhance earthquake preparedness and response in Sulawesi. This study contributes to a better understanding of earthquake patterns and potential seismic gaps in Sulawesi, which is crucial for developing improved risk mitigation strategies and supporting sustainable development policies.
Optimizing DBSCAN Parameters for Depth-Based Earthquake Clustering Using Grid Search Rushendra, Rushendra; Wijaya, Ody Octora; Yusuf, Mohamad; Setiyaji, Andri; Prabowo, Djoko
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6521

Abstract

This study addresses the challenge of accurately clustering earthquake events based on depth to better understand seismic activity patterns in Sulawesi from 2019 to 2023. Traditional clustering algorithms often fail to capture the complex spatial and depth-based structures of earthquake data. To overcome this, we employed the DBSCAN algorithm, which is well-suited for identifying irregularly shaped clusters and handling noise in spatial datasets. A key focus of this research is the systematic optimization of DBSCAN’s parameters—epsilon (ε) and minimum samples (min_samples)—using a grid search approach. Epsilon values varied from 0.1 to 0.5, and min_samples ranged from 6 to 60. The optimal parameters, determined using the Calinski-Harabasz (CH) index, were ε = 0.4 and min_samples = 54. Compared with previous heuristic settings, the optimized configuration produced better separated and more interpretable clusters. Using the optimized parameters, nine distinct clusters were identified, capturing meaningful patterns in both depth and magnitude. The results revealed that shallow earthquakes (0–20 km) tend to exhibit greater magnitude variation, with some clusters averaging magnitudes up to 3.7. This suggests a higher seismic hazard potential associated with brittle crustal activity. The findings contribute to seismic hazard analysis by providing a more robust understanding of three-dimensional earthquake distribution, aiding regional risk assessment and disaster preparedness efforts. These insights can support agencies such as BMKG and BPBD in hazard mapping, sensor deployment, and contingency planning for high-risk zones.
Co-Authors Aan Wahyu Anung YS Ardianto, Muhammad Rezanur Ariya Pramudita Awang, Nungky Dimas Surya Subrata Ellisa Agustina, Ellisa Firdyanto, Tri imaniah Aliat Lukman Hakim maniah Aliat5 Miftahul Huda Mohamad Yusuf Mohamad Yusuf Mohamad Yusuf Nungky Awang Candra Panca, Aji Permatasari, Eka Prabowo, Djoko Rahmad Hidayat Robbi Rahim Roy Mubarak Saipul Anwar Setiyaji, Andri Siti Maesaroh Wijaya, Ody Octora Wulandari, Ike Yuni Yakob Liklikwatil Yasin Efendi