Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Applied Data Sciences International Journal of Management Science and Information Technology (IJMSIT)
Ngatimin Ngatimin
Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Optimizing K-means Clustering with Seed Initialization for Osteoporosis Diagnosis Based on Family History Adiyah Mahiruna; Ngatimin Ngatimin; Rachmat Destriana
International Journal of Management Science and Information Technology Vol. 6 No. 1 (2026): January - June 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v6i1.6648

Abstract

World Osteoporosis Day (WOD) is celebrated on October 20 every year, to raise global awareness about the prevention, diagnosis, and treatment of osteoporosis. Urgency in Indonesia, the number of elderly people is projected to reach 71 million people in 2050, which will have an impact on increasing cases of osteoporosis. Therefore, the recommendations based on scientific evidence in this study aim to assist practitioners in preventing osteoporosis in adults and children. This study proposes a method of Improving K-Means Performance through Seeds. The performance of the K-Means clustering algorithm is highly dependent on the random selection of initial centroids, which can lead to unstable clusters, suboptimal local solutions, and increased iterations, particularly in medical datasets such as osteoporosis diagnosis based on family history. Therefore, there is a need for an optimized centroid initialization strategy that can improve clustering accuracy and stability without increasing computational complexity. The dataset used is the osteoporosis dataset as a testing dataset that can be accessed publicly Osteoporosis dataset. The novelty of this study lies in the introduction of Modified Average (MA) approach for centroid initialization, which eliminates random seed dependency and improves clustering stability without increasing computational complexity. From the results of nine experiments with the benchmarking dataset, it can be seen that the method proposed in this study indicates that practically the Proposed method has a tendency to perform better in Rand Index measurement compare to k-means in random seeds.
A Bi-LSTM Prediction Model Integrated with GIS for Spatiotemporal Malaria Endemicity Mapping and Early Warning in Indonesia Wellie Sulistijanti; Safaat Yulianto; Abdul Syukur; Ngatimin Ngatimin
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1483

Abstract

Malaria continues to be a major public health challenge in Indonesia, particularly in eastern provinces where transmission patterns are influenced by climatic variability, geographical heterogeneity, and historical incidence trends. This study proposes an integrated spatio-temporal malaria forecasting and early warning framework by combining Bidirectional Long Short-Term Memory (Bi-LSTM), Geographic Information Systems (GIS), and SHapley Additive exPlanations (SHAP). Monthly malaria incidence, climate variables, population data, and provincial spatial data from 12 endemic provinces in Indonesia during 2014–2025 were used. The data were preprocessed through incidence-rate conversion, outlier handling, log transformation, Min-Max normalization, and six-month sliding window segmentation. The proposed Bi-LSTM model was assesed using RMSE, sMAPE, and R², and compared againts Naive Forecasting, SARIMA, and Simple LSTM baselines. The model attained optimol global performance, with an RMSE of 0.0522, sMAPE of 18.39%, and R² of 0.9553. The provincial analysis shows good performance throughout most regions, including high-burden areas like Papua and West Papua, however a decline in relative accuracy was observed in West Nusa Tenggara due to near-zero incidence rates. SHAP analysis revealed that historical malaria incidence was the primary predictor, whereas rainfall emerged as the most significant climatic variable. GIS-based forecasting showed spatial patterns aligned with malaria epidemiology in Indonesia, with Papua exhibiting the gratest predicted incidence in December 2025. These findings demonstrate that the Bi-LSTM–GIS–SHAP framework can support malaria endemicity mapping, interpretable forecasting, and province-level early warning for targeted public health interventions.
Co-Authors Abdul Syukur Adiyah Mahiruna Destriana, Rachmat Safaat Yulianto Wellie Sulistijanti