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All Journal INTECOMS: Journal of Information Technology and Computer Science Jurnal Abdi Masyarakat Indonesia Malcom: Indonesian Journal of Machine Learning and Computer Science Geosains Kutai Basin
Jailani, Zakiul Fahmi
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Humanities Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Public Health

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Mapping the Golden Hour: A Spatiotemporal Analysis of Ambulance Response Time in Urban Jakarta Jailani, Zakiul Fahmi; Nurmadewi, Dita; Syumanjaya, Raden Bambang; Manik, Ni Kadek Sri
GEOSAINS KUTAI BASIN Vol. 6 No. 2 (2023)
Publisher : Geophysics Study Program, Faculty of Mathematics and Natural Sciences, Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/geofisunmul.v6i2.1188

Abstract

This study aims to map the ambulance response time in Jakarta city and assess the current ambulance to population ratio. The data used in this study include hospital point data retrieved from OpenStreetMap (OSM), manually digitized data points, and a database from community/non-government organizations that manage their own ambulance. The analysis was conducted using a combination of buffer, service area, and overlay tools in GIS (Geo-information System) software. The results show that the current ambulance to population ratio in Jakarta is inadequate, with only 78 ambulances available that can only serve a total population of 5,598,058 out of all 10,748,230 people in Jakarta. This means that at least 215 ambulances are needed to provide comprehensive coverage for the entire population. Furthermore, the golden time for ambulance response, as set by the Ministry of Health in Indonesia, is less than 15 minutes. However, the current ambulance to population ratio in Jakarta makes it difficult to meet this standard.
Hybrid Machine Learning Predicts Flooding Using Lstm And Random Forests On Geodata Jailani, Zakiul Fahmi; Nurmadewi, Dita
INTECOMS: Journal of Information Technology and Computer Science Vol 8 No 1 (2025): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v8i1.13991

Abstract

Flood prediction remains a critical concern in Indonesia, a nation frequently affected by seasonal deluges. This research aims to predict flood occurrences in five key provinces by employing a hybrid machine learning approach using Long Short-Term Memory (LSTM) networks and Random Forest models. Leveraging geospatial and temporal data from Petabencana.id, collected between January 2018 and February 2024, the study develops a predictive framework for flood forecasting. The analysis integrates flood depth and historical disaster data to estimate the time to the next flood, with predictions starting after the last data entry in February 2024. The model accurately predicted that Jakarta would experience flooding within 25–50 days post-February, a forecast corroborated by significant floods in April 2024. Other provinces, including Central Java and East Java, displayed longer flood risk windows extending further into the year. With a training accuracy of 99%, the model underscores its reliability in predicting flood events. This study emphasizes the strength of LSTM in capturing temporal patterns and the role of Random Forests in identifying key predictive features. The proposed model offers a valuable tool for disaster management agencies and local governments, enabling them to anticipate and mitigate flood impacts using real-time data from Petabencana.id.
Peningkatan Literasi Digital Tenaga Kependidikan melalui Pelatihan Microsoft 365 di Universitas Bakrie, DKI Jakarta Nurmadewi, Dita; Jailani, Zakiul Fahmi; Anggoro, Dimas Aryo; Cahyaningsih, Elin; Fortunisa, Ananda; Fitriyan, Rohmad; Juang, Muhammad Isman Sabar
Jurnal Abdi Masyarakat Indonesia Vol 6 No 1 (2026): JAMSI - Januari 2026
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jamsi.2551

Abstract

Kegiatan pengabdian kepada masyarakat ini dilatarbelakangi oleh rendahnya tingkat literasi digital tenaga kependidikan di lingkungan perguruan tinggi, khususnya dalam pemanfaatan aplikasi kolaboratif berbasis cloud untuk mendukung efisiensi administrasi. Kondisi awal menunjukkan bahwa pengelolaan dokumen dan koordinasi kerja masih dilakukan secara terpisah dan manual, sehingga belum mendukung pola kerja kolaboratif dan produktif. Kegiatan ini bertujuan untuk memberdayakan tenaga kependidikan melalui pelatihan Microsoft 365 berbasis praktik guna meningkatkan efisiensi kerja administratif dan kapasitas kolaborasi digital. Metode pelaksanaan berupa pelatihan tatap muka dengan pendekatan hands-on yang melibatkan peserta secara aktif dalam penyelesaian studi kasus administratif nyata. Evaluasi keberhasilan kegiatan dilakukan melalui pre-test dan post-test sebagai indikator peningkatan kapasitas peserta. Hasil kegiatan menunjukkan peningkatan nilai rata-rata dari 65% pada pre-test menjadi 78% pada post-test, yang mengindikasikan peningkatan pemahaman dan keterampilan digital peserta. Peningkatan tersebut berdampak pada perubahan pola kerja peserta yang lebih mandiri, kolaboratif, dan efisien dalam pengelolaan dokumen serta komunikasi kerja. Kegiatan ini berkontribusi pada penguatan kapasitas tenaga kependidikan dalam mendukung transformasi tata kelola administrasi berbasis teknologi di lingkungan perguruan tinggi.
Perbandingan Support Vector Machine dan Naïve Bayes untuk Klasifikasi Sentimen Ulasan E-Commerce: Comparison of Support Vector Machine and Naïve Bayes for E-Commerce Review Sentiment Classification Nurmadewi, Dita; Jailani, Zakiul Fahmi; Rafi, Haris; Anggoro, Dimas Aryo; Setiowati, Dewi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2648

Abstract

Ulasan pelanggan di platform e-commerce memuat informasi penting tentang pengalaman pengguna terhadap produk dan layanan. Namun, mengingat jumlahnya sangat besar, analisis manual tidak efisien. Klasifikasi sentimen berbasis machine learning dapat secara otomatis mengidentifikasi opini dari teks ulasan. Penelitian ini ingin melakukan perbandingan antara performa Support Vector Machine (SVM) dan Naïve Bayes dalam melakukan klasifikasi sentimen pada ulasan di platform e-commerce. Dataset terdiri atas 11.606 ulasan pelanggan yang bersumber dari repositori dataset publik. Tahap pra-pemrosesan mencakup case folding, tokenization, penghilangan stopword, serta stemming. Fitur teks ditampilkan memakai Term Frequency–Inverse Document Frequency (TF-IDF). Kinerja model dievaluasi berdasarkan skema 5-fold cross-validation menggunakan metrik accuracy, precision, dan recall, serta F1-score. Hasil eksperimen menemukan algoritma Support Vector Machine mempunyai performa lebih unggul jika dibandingkan dengan Naïve Bayes, di mana perolehan nilai accuracy masing-masing mencapai 0.8717 dan 0.8555. Temuan ini sekaligus menunjukkan Support Vector Machine mempunyai kemampuan generalisasi yang lebih baik dalam membedakan kelas sentimen pada data ulasan e-commerce berbasis teks.
Co-Authors Ananda Fortunisa Cahyaningsih, Elin Dimas Aryo Anggoro Fitriyan, Rohmad Juang, Muhammad Isman Sabar Manik, Ni Kadek Sri Nurmadewi, Dita Rafi, Haris Setiowati, Dewi Syumanjaya, Raden Bambang