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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Sains Teknologi dan Sistem Informasi Jurnal Teknik Informatika EdLib Journal (Education and Library Journal)
Akhiyar Waladi
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Computer Science & IT Decision Sciences, Operations Research & Management Library & Information Science Other

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Comparative Analysis of Triangulation Methods for Optimal Solutions to the Art Gallery Problem Marzal, Jefri; Niken Rarasati; Waladi, Akhiyar; Perdana, Yogi
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 1 (2025): Maret 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i1.10749

Abstract

Triangulation is the process of breaking down an n-sided polygon into triangles and it is necessary in deciding the optimal count and the position of guards in the Art Gallery Problem (AGP) There is a theoretical limit that has been established which states that the number of required guards needed to keep an eye on such a polygon is ⌊n/3⌋ and this research considers this as the limit. Among various triangulation methods, Ear Clipping and Minimum Weight are two primary approaches frequently used to achieve optimal solutions. Nonetheless, its comparison with other methods, more particularly the amount of guards required for the maximum theoretical figure, is still a gap in literature. The aim of this research is to create an AGP simulation program and test it against the theoretical upper bound, determining the number of guards required. 228 simple polygons with vertices varying between 10 and 110 were utilized in this research. The polygons were classified into three groups based on the ratio of convex to concave vertices: less concave vertices, equal amount of concave and convex vertices and vice versa. Result study shows that the Ear Clipping method is significantly superior to Minimum Weight in reducing guard requirements. Practically speaking, these advancements are important for the design of engineering systems such as surveillance systems and the surveillance of public spaces. In the context of building security system design and monitoring of large areas, these conclusions are of utmost importance.
Analisis Prediktif Tren Pendidikan di Indonesia Menggunakan KNN Studi Kasus Data Pendidikan 2021-2023 Nasution, Mukhtada Billah; Akhiyar Waladi; Ulfa Khaira; Pradita Eko Prasetyo Utomo
Education Library Vol. 1 No. 2 (2025): Education and Library Journal
Publisher : UPT Perpustakaan Universitas Jambi

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Abstract

This research focuses on the importance of education in improving the competitiveness of the younger generation in Indonesia, especially in facing the challenges of globalization and the digital revolution. Education trends in Indonesia during the 2021-2023 period have been dominated by two main factors, namely digitalization and equal access to education. A data-driven approach is used to predict education trends in 2024, using the K-Nearest Neighbor (KNN) algorithm to analyze data from the Central Statistics Agency (BPS) regarding the percentage of the population aged 25 years and over who have at least a high school education, categorized by gender. The result of this research will predict the trend of education in each region in 2024 whether it is decreasing, stable, or increasing. Through data collection and literature study, this research identifies relevant patterns and presents statistically-based predictions that can serve as a reference for stakeholders in the development of education in Indonesia. The results of this study are also expected to provide insights for policymakers in formulating effective strategies to address the education gap and promote inclusive digitalization..
Prediksi Nilai Ekspor Migas Indonesia menggunakan Metode ARIMA Hasby Kuswanto; Pradita Eko Prasetyo Utomo; Ulfa Khaira; Akhiyar Waladi
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 5 No. 1 (2025): April 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v5i1.4103

Abstract

This study aims to predict Indonesia's oil and gas (migas) export values using the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Long Short-Term Memory (LSTM) methods. Time series data from Statistics Indonesia (BPS) was utilized to develop an optimal prediction model. The selected SARIMA model, SARIMA(1,1,1)(1,1,1,12), was chosen based on the lowest Akaike Information Criterion (AIC) value. Meanwhile, the LSTM model was developed to capture more complex patterns in time series data. The forecasting results indicate that the SARIMA model provides higher accuracy compared to LSTM based on the Mean Absolute Percentage Error (MAPE), although LSTM demonstrated lower Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). This study emphasizes that the choice of forecasting model should align with the characteristics of the data, where SARIMA is more suitable for oil and gas export data with seasonal patterns. These forecasting results can be utilized to support economic policy planning, optimize investments in the oil and gas sector, and mitigate global market fluctuation risks.
Peningkatan Akurasi Klasifikasi Tutupan Lahan Menggunakan Random Forest pada Data Sentinel-2 di Jambi Waladi, Akhiyar
JURNAL FASILKOM Vol. 15 No. 1 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i1.8886

Abstract

Klasifikasi tutupan lahan yang akurat memainkan peran penting dalam pemantauan lingkungan, perencanaan perkotaan, dan pengelolaan sumber daya berkelanjutan. Dengan meningkatnya kekhawatiran terhadap perubahan penggunaan lahan dan degradasi ekologis, pengembangan metode klasifikasi yang efektif menjadi semakin penting, terutama di wilayah yang mengalami transformasi lanskap secara cepat. Penelitian ini mengevaluasi kinerja tujuh algoritma machine learning (Random Forest, Extra Trees, Logistic Regression, Decision Tree, Naive Bayes, SGD Classifier, dan LightGBM) untuk klasifikasi tutupan lahan menggunakan data satelit Sentinel-2 di wilayah Jambi. Studi ini menggunakan 23 fitur, termasuk 10 band spektral dan 13 indeks spektral, dengan data yang dikumpulkan selama Q4 2024. Hasil menunjukkan bahwa Random Forest mencapai kinerja terbaik secara keseluruhan dengan akurasi 85.91% dan weighted F1-score 85.48%, diikuti oleh Extra Trees dengan akurasi 84.45%. Algoritma berbasis pohon keputusan menunjukkan kemampuan yang lebih unggul dalam membedakan area perkotaan, vegetasi, dan badan air, meskipun semua algoritma menghadapi tantangan dengan kelas minoritas. Temuan ini merepresentasikan peningkatan signifikan dibandingkan pendekatan sebelumnya yang hanya mencapai akurasi 37.7%-66.9% menggunakan indeks vegetasi tunggal. Peningkatan akurasi klasifikasi memungkinkan pemantauan yang lebih efektif terhadap deforestasi, ekspansi perkotaan, dan perubahan ekosistem di wilayah tropis, memberikan dukungan penting bagi kebijakan pengelolaan lahan berbasis bukti dan strategi konservasi di lanskap kompleks seperti Jambi.
Pengelompokan Provinsi Di Indonesia Berdasarkan Rasio Penggunaan Gas Rumah Tangga Pada Tahun 2023 Menggunakan Hierarchical Clustering Nasution, Afdal Aditya; Eko Prasetyo Utomo, Pradita; Ulfa Khaira; Akhiyar Waladi
JEKIN - Jurnal Teknik Informatika Vol. 5 No. 1 (2025)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jekin.v5i1.1232

Abstract

Penggunaan energi gas rumah tangga merupakan aspek penting yang mencerminkan aksesibilitas dan distribusi energi di berbagai wilayah Indonesia. Penelitian ini bertujuan untuk mengelompokkan provinsi-provinsi di Indonesia berdasarkan pola penggunaan gas rumah tangga menggunakan metode klasterisasi hierarki dengan pendekatan average linkage. Data yang digunakan bersumber dari Badan Pusat Statistik (BPS) dan telah melalui proses praproses untuk memastikan kebersihan dan konsistensi data. Evaluasi hasil klasterisasi dilakukan dengan membandingkan nilai rata-rata Silhouette Coefficient pada jumlah klaster yang berbeda, menunjukkan bahwa dua klaster merupakan jumlah optimal, dengan pemisahan antar kelompok yang signifikan dan kohesi yang tinggi dalam setiap klaster. Hasil klasterisasi ini memberikan gambaran yang jelas mengenai pola penggunaan gas di Indonesia, dengan rata-rata rasio penggunaan gas rumah tangga tahun 2023 pada klater 1 adalah 90,02 atau 90,02%. Dan rata-rata rasio penggunaan gas rumah tangga tahun 2023 pada klater 2 adalah 2,3 atau 2,3%.
Co-Authors Eko Prasetyo Utomo, Pradita Hasby Kuswanto Jefri Marzal Khaira, Ulfa Nasution, Afdal Aditya Nasution, Mukhtada Billah niken rarasati Perdana, Yogi Utomo, Pradita Eko Prasetyo