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All Journal Jurnal Ilmu Komputer dan Agri-Informatika Bulletin of Computer Science Research Prosiding Seminar Nasional Official Statistics
Ahmad, Hafidlotul Fatimah
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Klasterisasi Kecamatan di Jabodetabek Berdasarkan Potensi Pengembangan Pasangan Tanaman Sayuran Tahun 2020 Ahmad, Hafidlotul Fatimah; Soim, Ahmad
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.1941

Abstract

Per capita vegetable consumption in Indonesia has increased from 54,291 kg in 2018 to 58,477 kg in 2021. This increase highlights the need for more efficient vegetable cultivation systems, such as polyculture, where multiple types of plant are grown in one location. However, the suitability of plants for polyculture systems has traditionally been determined using approximate methods. To address this, the research utilizes the ECLAT algorithm with vegetable production data from BPS to identify which crops are frequently grown together in each sub-district. Furthermore, to identify areas with potential for developing vegetable crop pairs, clustering was carried out using the K-Medoids algorithm. The findings reveal that the most commonly paired vegetable plants in sub-district of Greater Jakarta are "Cucumber & Long Beans" and "Spinach & Kale". These sub-districts were grouped based on crop pair production into 3 clusters with high, medium and low production levels. The research concludes that the highest level of vegetable crop pair production is in Bogor Regency.
Pengembangan Modul Front-End KMS Desa Digital untuk meningkatkan adopsi Inovasi Digital pada Desa di Indonesia Nurhadryani, Yani; Nuryantika, Fitria; Hermadi, Irman; Ahmad, Hafidlotul Fatimah
Jurnal Ilmu Komputer dan Agri-Informatika Vol 12 No 1 (2025)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.12.1.72-78

Abstract

Indonesia memiliki jumlah desa yang sangat besar, yaitu sebanyak 83.794 desa, sehingga transformasi digital memegang peranan penting dalam meningkatkan kesejahteraan masyarakat pedesaan. Desa digital adalah desa yang menerapkan teknologi informasi untuk mendorong efisiensi pelayanan publik, penguatan ekonomi masyarakat desa, serta peningkatan kualitas hidup masyarakat desa. Namun, rendahnya literasi digital dan terbatasnya kapasitas sumber daya manusia masih menjadi kendala dalam implementasi desa digital. Penelitian sebelumnya yang bekerja sama dengan FAO pada program Digital Village Initiative (2023) telah menghimpun data melalui pendekatan etnografi terhadap 160 desa digital dan 100 inovasi digital yang dikategorikan dalam 10 kelompok, seperti Agri-Food Marketing and E-commerce, E-Government, Smart Farming, dan Social Service. Sayangnya, informasi desa digital saat ini tersebar secara tidak terstruktur sehingga sulit diakses dan dimanfaatkan oleh desa lain. Penelitian ini bertujuan mengembangkan platform Knowledge Management System (KMS) desa digital menggunakan pendekatan Prototyping melalui tahapan komunikasi, perencanaan cepat, desain, pembuatan prototipe, dan evaluasi. KMS ini ditujukan bagi inovator, perangkat desa, dan masyarakat desa, serta dapat diakses secara publik. Modul yang dikembangkan memuat informasi inovasi digital dalam 10 kategori beserta deskripsinya, profil inovator, profil desa digital, serta fitur asesmen kesiapan digital desa. Hasil ini diharapkan dapat mempercepat diseminasi pengetahuan dan adopsi inovasi digital antar desa di Indonesia.
Analisis Klasifikasi Kesiapan Digital Desa Menggunakan Decision Tree dan Pemetaan Spasial Fatimah Ahmad, Hafidlotul; Firdawanti, Aulia Rizki; Agustiani, Nur
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.741

Abstract

Digital transformation at the village level is a strategic element in promoting equitable development and improving public service delivery. However, the level of digital readiness across regions remains uneven. This study aims to classify the digital readiness of villages in West Java Province by utilizing data from Open Data Jabar (opendata.jabarprov.go.id) related to the number of digital villages, internet access, and village development strata. A Decision Tree classification algorithm was employed to categorize regions into two readiness classes: high and low. The modeling results indicate that the number of self-reliant (mandiri) villages and the percentage of villages with internet access are the most influential variables in the classification. Although internet infrastructure is available in most areas, it does not always correspond to the level of village digitalization. Districts with high internet access but a low number of self-reliant villages are still classified as having low readiness. The model achieved an accuracy of 83%, although its performance in identifying the high readiness class was limited due to class imbalance in the dataset. Spatial visualization was also used to highlight regional disparities in digital readiness. This study provides an early contribution to digital readiness mapping of villages using a machine learning approach in Indonesia.
Co-Authors Aulia Rizki Firdawanti Irman Hermadi Nur Agustiani Nuryantika, Fitria Soim, Ahmad Yani Nurhadryani