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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Manajemen Informatika dan Sistem Informasi
Ita Arfyanti
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Computer Science & IT Engineering

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ANALISIS SENTIMEN KEPUASAN LAYANAN KANTOR DESA LINGGANG BIGUNG MENGGUNAKAN METODE LEXICON-BASED Marentinus, Christian; Ita Arfyanti; Wahyuni
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 9 No. 1 (2026): MISI Januari 2026
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v9i1.1914

Abstract

Analisis sentiment lexicon-based Evaluasi layanan publik tingkat desa umumnya masih bergantung pada kuesioner terstruktur yang kurang mampu menangkap persepsi masyarakat secara mendalam, khususnya yang disampaikan melalui narasi terbuka. Penelitian ini bertujuan menganalisis sentimen masyarakat terhadap layanan Kantor Desa Linggang Bigung menggunakan pendekatan analisis sentimen lexicon-based untuk mengidentifikasi kekuatan dan kelemahan layanan. Data dikumpulkan melalui kuesioner daring berisi pertanyaan terbuka yang diisi oleh 52 responden, menghasilkan 150 segmen teks naratif. Analisis dilakukan melalui tahapan text preprocessing dan pencocokan kata menggunakan leksikon InSet. Hasil analisis menunjukkan bahwa persepsi masyarakat didominasi sentimen positif (63%), diikuti sentimen negatif (25%) dan netral (12%). Sentimen positif terutama berkaitan dengan kecepatan pelayanan dan keramahan petugas, sementara sentimen negatif berkaitan dengan kondisi fasilitas dan waktu tunggu. Perbandingan hasil klasifikasi dengan pendekatan berbasis AI menunjukkan tingkat kesesuaian yang tinggi, mengindikasikan bahwa metode lexicon-based mampu merepresentasikan pola sentimen masyarakat secara memadai. Temuan ini menegaskan bahwa analisis sentimen lexicon-based merupakan pendekatan yang efektif, interpretatif, dan layak diterapkan pada konteks pemerintahan desa dengan keterbatasan data dan sumber daya komputasi.
Nonlinear Modeling of Agricultural and Environmental SDG Indicators in ASEAN Using Extreme Learning Machine Algorithms Ita Arfyanti; Muhammad Ibnu Sa'ad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7041

Abstract

This study presents a data-driven approach for modeling Sustainable Development Goal (SDG) indicators in ASEAN countries using the Extreme Learning Machine (ELM) algorithm. Focusing on SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), and SDG 15 (Life on Land), we utilized FAOSTAT datasets from 2020 to 2024 to forecast key indicators such as undernourishment, water use efficiency, and forest area. ELM, known for its rapid learning speed and capability to model nonlinear relationships, outperformed baseline models Linear Regression and Support Vector Machine (SVM) in terms of R² score, RMSE, and MAE. Specifically, ELM achieved R² values exceeding 0.93, with up to 54% RMSE reduction compared to linear models. The model successfully captured national development trends, including deforestation in Indonesia and Cambodia, water stability in Brunei, and varied progress in sustainable agriculture across the region. This study underscores the effectiveness of the Extreme Learning Machine (ELM) in forecasting Sustainable Development Goal (SDG) indicators and provides actionable insights to support evidence based policy planning, particularly in resource-constrained settings. The findings demonstrate that ELM’s combination of interpretability, computational efficiency, and scalability positions it as a highly valuable tool for real-time monitoring of sustainable development across Southeast Asia.
Co-Authors Marentinus, Christian Muhammad Ibnu Sa'ad WAHYUNI