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All Journal Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Jurnal Fourier Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) BAREKENG: Jurnal Ilmu Matematika dan Terapan JITK (Jurnal Ilmu Pengetahuan dan Komputer) JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Pendidikan Matematika (Kudus) J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika EIGEN MATHEMATICS JOURNAL Community Development Journal: Jurnal Pengabdian Masyarakat LOSARI: Jurnal Pengabdian Kepada Masyarakat Jurnal Litbang Edusaintech Fraction: Jurnal Teori dan Terapan Matematika Tepis Wiring : Jurnal Pengabdian Masyarakat Journal of Data Insights Prosiding Seminar Nasional Unimus
Prizka Rismawati Arum
Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Smart Village Application untuk Meningkatkan Pelayanan Publik Pemerintah Desa Katonsari Kabupaten Demak M. Al Haris; Prizka Rismawati Arum; Dannu Purwanto; Ali Imron; Linda Puspitasari; Miftakhul Haris
LOSARI: Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 2 (2023): Desember 2023
Publisher : LOSARI DIGITAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53860/losari.v5i2.161

Abstract

Pemerintah Desa Katonsari Kabupaten Demak, Provinsi Jawa Tengah, berkomitmen untuk memberikan pelayanan terbaik kepada warganya. Saat ini, pelayanan publik masih dilakukan secara manual yang mengakibatkan ketidakefektifan dan ketidakefisienan dalam pelayanan. Selain itu, sumber daya manusia di Desa Katonsari masih belum memiliki keterampilan teknologi informasi, sehingga perlu meningkatkan kemampuan dalam memanfaatkan teknologi. Untuk mengatasi permasalahan tersebut, tim pengabdian Universtas Muhammadiyah Semarang memberikan solusi dalam bentuk "Smart Village Application" untuk meningkatkan pelayanan publik pemerintah Desa Katonsari. Tahapan kegiatan diawali dengan identifikasi kebutuhan desa dengan pendekatan wawancara, perancangan sistem smart village application, pelatihan penggunaan sistem, dan evaluasi terhadap kinerja dan keefektifan sistem. Sasaran kegiatan ditujukan kepada perangkat desa dan beberapa warga Desa Katonsari sejumlah 30 orang. Berdasarkan kegiatan yang telah dilaksanakan, peserta kegiatan terlihat antusian dan puas terhadap program yang dilaksanakan. Hal tersebut didasarkan pada hasil survei kepuasan yang Tim pengabdian lakukan setelah selesai kegiatan.
MODELLING SCHOOL DROPOUT RATES IN WEST JAVA PROVINCE WITH MIXED GEOGRAPHICALLY TEMPORALLY WEIGHTED REGRESSION Rismawati Arum, Prizka; Maharani, Endang Tri Wahyuni; Fatimahthus Zahra, Diandra; Utami, Tiani Wahyu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0123-0136

Abstract

School dropout is a problem in the education sector that can hinder the progress of the quality of human resources and the competitiveness of the nation. West Java Province has the highest school dropout rates among all provinces in Indonesia. The data on school dropout rates exhibit spatial and temporal variations. Additionally, the potential differences between regions allow for the occurrence of diverse data that can be addressed locally and globally. Mixed Geographically Temporally Weighted Regression (MGTWR) is an extension of the GWR method that can produce parameters that are both local and global for each location and time. So, the objective of this research is to obtain factors that have a local and global influence on the school dropout rate in West Java Province using the Mixed Geographically Temporally Weighted Regression method. In this study, the data used includes school dropout rates in West Java Province from 2018 to 2022. The data used is sourced from the official statistical data website of the Ministry of Education, Culture, Research and Technology, and the official website of the West Java Province Central Statistics Agency. The results of the MGTWR modeling show that globally influential variables include the percentage of the poor population, population density, unemployment rate, and average length of schooling, which have local effects. Based on the MGTWR model, the Fixed Kernel Gaussian weighting function is the best model for modeling school dropout rates in regencies/cities in West Java, with an RMSE value of 0.0755 and R-squares of 92.09%.
Implementation of K-Means to Classify Poverty Based on Housing Characteristics in Central Java in 2021 Setyaningsih, Laras Indah; Wulandari, Anjelina Rafika; Arum, Prizka Rismawati
Jurnal Pendidikan Matematika Vol 6, No 1 (2023): Jurnal Pendidikan Matematika (Kudus)
Publisher : Universitas Islam Negeri Sunan Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21043/jpmk.v6i1.19529

Abstract

Poverty is a condition in which a person's inability to meet basic needs such as food, clothing, shelter, and education so that he is unable to guarantee his own survival. To support the successful implementation of development programs, especially those aimed at reducing poverty, grouping districts/cities using cluster analysis can be assisted. Cluster analysis can be carried out to identify how the poverty rate is based on housing characteristics in Central Java which can be taken into consideration so that development programs are more targeted. Cluster analysis is a grouping method in which a group has the same characteristics, while between groups have different characteristics. K-means is one of the algorithms in data mining that can be used for grouping/clustering. The purpose of this study was to determine the classification of poverty in Central Java districts/cities based on housing indicators which include the floor area of the house, building materials for the widest floor, sources of drinking water, main building materials for roofs, and main fuel for cooking. This study yielded three clusters, with cluster 1 consisting of 22 districts and cities, cluster 2 consisting of 5 districts and cities, and cluster 3 consisting of 8 districts and cities. Cluster 1 grouping indicators were based on the sources of drinking water and the type of fuel used for cooking, cluster 2 grouping indicators were based on the size of the house's floor plan, and cluster 3 grouping indicators were based on the materials used to construct the house's widest floor and its main roof. Kemiskinan merupakan kondisi dimana ketidakmampuan seseorang untuk memenuhi kebutuhan pokok seperti pangan, sandang, papan dan pendidikan sehingga tidak mampu menjamin kelangsungan hidupnya sendiri. Untuk menunjang keberhasilan pelaksanaan program-program pembangunan, khususnya yang ditujukan untuk mengurangi kemiskinan dapat dibantu dengan mengelompokkan kabupaten/kota dengan analisis cluster. Analisis cluster dapat dilakukan untuk mengenali bagaimana tingkat kemiskinan berdasarkan karakteristik perumahan di Jawa Tengah yang dapat dijadikan pertimbangan agar program-program pembangunan lebih tepat sasaran. Analisis cluster merupakan suatu metode pengelompokan dimana dalam suatu kelompok mempunyai karakteristik yang sama, sedangkan antar kelompok mempunyai karakteristik yang berbeda. K-means merupakan salah satu algoritma dalam data mining yang dapat digunakan untuk mengelompokkan/clustering. Tujuan penelitian ini ialah untuk mengetahui pengelompokan kemiskinan Kabupaten/Kota Jawa Tengah berdasarkan indikator perumahan yang meliputi luas lantai rumah, bahan bangunan untuk lantai terluas, sumber air minum, bahan bangunan utama untuk atap rumah, dan bahan bakar utama untuk memasak. Penelitian ini menghasilkan 3 cluster dengan cluster 1 memiliki anggota 22 Kabupaten/Kota, cluster 2 memiliki anggota 5 Kabupaten/ Kota, cluster 3 memiliki anggota 8 Kabupaten/Kota. Indikator pengelompokkan cluster 1 didasarkan kepada sumber air minum dan bahan bakar untuk memasak yang digunakan, indikator pengelompokan cluster 2 didasarkan kepada luas lantai rumah, sedangkan indikator pengelompokkan cluster 3 didasarkan kepada bahan bangunan untuk lantai terluas dan bahan bangunan utama untuk atap rumah.
HYBRID RESAMPLING METHOD AND HYPERPARAMETER OPTIMIZATION FOR HIV/AIDS PREDICTION: EVIDENCE FROM EIGHT MACHINE-LEARNING MODELS Lydia Nur Sa'adah; Fatkhurokhman Fauzi; Prizka Rismawati Arum; M Al Haris; Yan Nazala Bisoumi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7533

Abstract

HIV/AIDS remains a global health challenge with continuously increasing infection rates, highlighting the importance of accurate prediction models to support prevention and early detection. However, the development of such models is often constrained by class imbalance and irrelevant features. This study aims to improve HIV/AIDS infection prediction by integrating feature selection, data balancing techniques, and eight machine learning algorithms. Feature selection was performed using Mutual Information and Chi-Square to identify the most relevant features. The dataset used was the HIV/AIDS Infection Prediction Dataset from Kaggle, consisting of 2,139 instances and 23 features, with an imbalanced distribution of 1,618 non-infected and 521 infected cases. The dataset was divided into 80% training data and 20% testing data, with resampling applied only to the training set to prevent data leakage. Three resampling scenarios were evaluated: no sampling, SMOTE, and SMOTE-ENN. Hyperparameter tuning was conducted using Bayesian Optimization integrated with 5-fold Cross-Validation to improve model robustness and reliability. Eight machine learning algorithms were evaluated, including Decision Tree, Random Forest, AdaBoost, Gradient Boosting, XGBoost, LightGBM, K-Nearest Neighbors, and Logistic Regression. The results show that SMOTE-ENN combined with hyperparameter optimization significantly improved model performance. The best model, Gradient Boosting + SMOTE-ENN, achieved 96.1% accuracy, 94.8% precision, 98.4% recall, and 96.5% F1-score. These findings indicate that the proposed integrated framework is highly effective for predicting HIV/AIDS infection and has strong potential to support early diagnosis and data-driven decision-making in healthcare.
Co-Authors Agi Khoerunnisa Alambara, Ach Ridoi Alfidayanti, Ina Alfisyahrina Hapsery Ali Imron Ali Imron Alwan Fadlurohman Amri, Saeful Andy Purnomo, Eko Anisa Putri Arla Vatwa Lubu Aqsal Maulana Ariska Fitriyana Ningrum Arya Praditya Azizah, Apipah Nur Cika Awani Ayuwida Cintadea Amanda Dwi Aryani Dannu Purwanto Dhani, Oktaviana Rahma Dheanyta Alif Shafira Dominikus Leki Sogen Dwi Rahmadini Eko Andy Purnomo Elvia Nanda Sofiyanti Endang Tri Wahyuni Maharani Erlinda, Relly Evida Oktaviana Fatimahthus Zahra, Diandra Fauzi, Fatkhurokhman Fauzi, Fatkhurrokhman Fenny Amalia Adani fitriani, ine Fitriyah, Novina Indah Gautama, Rahmad Putra Haris, M Al Indah Fitriyani Indah Manfaati Nur Indah Manfaati Nur Indah Sulistiya Indriani, Anita Retno Isna Nur Azizah Izzadin Muhamad Nur Irfan Jihan Syafiqoh, Amalia Kurnia Nur Annisa Laila Khoirun Nisa Linda Puspitasari Lisa Anggraini Lydia Nur Sa'adah M. Al Haris Manfaati Nur, Indah Miftakhul Haris Mohammad Ridwan Muhammad Saifudin Nur Nerisa Rahma Nursamsiah Nursamsiah Pratiwi, Alda Putri Prissy Nusaiba Yulisa Puspitasari, Listi Putro, Dwi Purnomo Rahmi, Mulya Asy-syifa Rizky Utami, Hanief Rochdi Wasono Rochdi Wasono Setyaningsih, Laras Indah Siswahyudianto Siti Mutiah Siva Alfian Sugito Sugito Supriyadin, Supriyadin Tiani Wahyu Utami Tresiani Yunitasari Ulinuha, Samikoh Velia Arni Widyasari Wisudawati, Dinda Tri Wulandari, Anjelina Rafika Yan Nazala Bisoumi Yuciana Wilandari Yulia Fitri