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All Journal Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Sistem Teknik Industri Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Bulletin of Computer Science Research Prosiding Seminar Nasional Official Statistics Jurnal Pemberdayaan Masyarakat
Ahmad, Hafidlotul Fatimah
Computer Science Study Program, School Of Data Science, Mathematics, And Informatics, IPB University, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Mathematics 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 Nuryantika, Fitria; Hermadi, Irman; Ahmad, Hafidlotul Fatimah; Nurhadryani, Yani
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 12 No. 1 (2025)
Publisher : Sekolah Sains Data, Matematika, dan Informatika. 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.
Hedging Strategy Analysis of GOTO Stock Using Collar, Bear Put Spread, and Long Strangle Agustiani, Nur; Wahyu, Sri; Firdawanti, Aulia Rizki; Ahmad, Hafidlotul Fatimah
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.34092

Abstract

This study compares the performance of three hedging strategies, Collar, Bear Put Spread, and Long Strangle, in a case study of PT GoTo Gojek Tokopedia Tbk (GOTO) stock. The analysis focuses on the risk management effectiveness and profit potential of these strategies within an emerging market context. The research utilizes weekly stock price data from July 2023 to June 2024 (54 observations). The methodological procedures include calculating returns and volatility, testing return normality using the Shapiro-Wilk test, determining European option prices using the Black-Scholes model with a 6% risk-free interest rate, and conducting profit simulations. The findings indicate that the Collar strategy provides maximum protection against stock price declines, albeit with limited profit potential. The Bear Put Spread strategy proves effective in generating returns during moderate price decreases while offering lower risk and cost. Conversely, the Long Strangle strategy possesses high profit potential during significant price volatility but carries the risk of total loss if stock prices remain stagnant. As a comprehensive comparison of these three option strategies applied to GOTO stock, this study recommends the Collar strategy as the optimal choice for risk-averse investors during bearish trends.
Empowering Digital Literacy through Computational Thinking, AI, and Renewable Energy in a Water-Scarce School Medria Kusuma Dewi Hardhienata; Wulandari Wulandari; Sheila Tobing; Andrew Schauf; Sita Rahmani; Gabriella Lumban Siantar; Hafidlotul Fatimah Ahmad; Auzi Asfarian; Takhta Pandu Padmanagara; Emir Raya Syuhada; Syaefudin Suminto; Iffa Mutmainah
Jurnal Pemberdayaan Masyarakat Vol 5, No 2 (2026)
Publisher : Yayasan Keluarga Guru Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46843/jpm.v5i2.703

Abstract

Ongoing advances in digital technology require teachers to adapt and develop their technological competence continuously. However, a gap in technological understanding remains between teachers in rural and urban areas, particularly in Indonesia. SMA Negeri 1 Sukanagara, as the only public high school in its region, faces challenges related to water scarcity and limited access to energy, which affect both learning activities and the school community's basic needs. This community service program aims to enhance teachers' digital literacy through training in Computational Thinking (CT), Artificial Intelligence (AI), and Renewable Energy prior to the implementation of a smart rainwater-harvesting system at the school. The methods used included a survey, training sessions, and evaluation using a problem-based approach combined with pre-test and post-test assessments. A problem-based learning approach confirmed that the CT training had helped teachers to develop more structured problem-solving skills. Results of the test also show that AI and Renewable Energy training improved participants' understanding by an average of 17.08% and 14.28%, respectively, compared to their initial scores. These improvements demonstrate the potential of training programs like these to enhance teachers' understanding and better prepare them for implementing emerging technologies in schools.
Analisis Klasifikasi Kesiapan Digital Desa Menggunakan Decision Tree dan Pemetaan Spasial Hafidlotul Fatimah Ahmad; Aulia Rizki Firdawanti; Nur Agustiani
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.
Analisis Perbandingan Kinerja Algoritma K-Means dan K-Medoids dengan Reduksi Dimensi PCA pada Indikator Kesehatan dan Sosial Aulia Rizki Firdawanti; Hafidlotul Fatimah Ahmad; Nur Agustiani
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.v5i5.742

Abstract

Public health in West Java faces complex challenges, including disparities in healthcare access, malnutrition, and socio-economic inequalities across districts. These conditions require data-driven analysis to identify patterns of disparity and provide evidence-based guidance for policy intervention. This study aims to cluster districts/cities in West Java based on health and social indicators using Principal Component Analysis (PCA) for dimensionality reduction, followed by K-Means and K-Medoids algorithms for clustering. Data from 27 districts/cities during 2019–2024 were analyzed after standardization. PCA extracted two principal components explaining 61.4% of the total variance. Scree plot and silhouette results indicated three optimal clusters. Comparative analysis revealed that the average silhouette score of K-Means was 0.31, while K-Medoids achieved a higher score of 0.34, suggesting more stable and robust partitioning against outliers. In 2024, Cluster 1 consisted of regions with adequate healthcare facilities and lower prevalence of underweight children; Cluster 2 grouped regions with limited health infrastructure and higher malnutrition problems, while Cluster 3 showed intermediate conditions. Therefore, K-Medoids outperformed K-Means by producing more consistent clustering across years. These findings offer practical recommendations: Cluster 2 should be prioritized for interventions such as improving primary healthcare access and nutrition programs, Cluster 1 requires maintenance of service quality, and Cluster 3 should be targeted for gradual reinforcement.
A Generalized Eyring-Weibull Model for Degradation Analysis of Lithium-Ion Batteries Under Multi-Stressor Conditions Nur Faizatus Sa'idah; Hafidlotul Fatimah Ahmad
Jurnal Sistem Teknik Industri Vol. 28 No. 3 (2026): JSTI Volume 28 Number 3 July 2026
Publisher : TALENTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jsti.v28i3.26011

Abstract

Degradation analysis of lithium-ion batteries is a critical aspect of understanding the aging behaviors and reliability of energy storage systems. Although purely data-driven approaches are widely utilized for their high short-term predictive accuracy, they often function as black-box models that fail to capture the underlying physicochemical mechanisms of capacity fade under dynamic operational conditions. To address these limitations, this study uses a Generalized Eyring-Weibull model for lithium-ion battery degradation analysis under multi-stressor conditions. The proposed analytical approach explicitly integrates three key operational stress variables simultaneously: temperature, State of Charge (SoC), and discharge current (C-rate). Model validation was performed using the NASA Prognostics Center of Excellence (PCoE) battery dataset across distinct discharge profiles (2A and 4A). Physical parameters—including activation energy (Ea) and stress exponents—were extracted via L-BFGS-B optimization, while the long-term capacity fade trajectory was tracked using Miner’s Rule. The evaluation results demonstrate that this generalized physics-based model accurately maps actual degradation trends, yielding high R2 values on State of Health (SoH) tracking. Furthermore, the model showcases superior robustness in capturing accelerated aging from high current loads using a single universal equation without requiring separate data retraining. By bridging empirical data with fundamental kinetic principles, this study provides an interpretable and robust analytical methodology for advanced predictive maintenance strategies.
Co-Authors Andrew Schauf Aulia Rizki Firdawanti Auzi Asfarian Emir Raya Syuhada Gabriella Lumban Siantar Hardhienata, Medria Kusuma Dewi Iffa Mutmainah Irman Hermadi Nur Agustiani Nur Agustiani Nur Faizatus Sa'idah Nuryantika, Fitria Sita Rahmani Soim, Ahmad Sri Wahyu Syaefudin Suminto Takhta Pandu Padmanagara Tobing, Sheila Wulandari Wulandari Yani Nurhadryani