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All Journal JUSIFO : Jurnal Sistem Informasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Febi Nur Salisah
Universitas Islam Negeri Sultan Syarif Kasim Riau
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Pendekatan Machine Learning: Analisis Sentimen Masyarakat Terhadap Kendaraan Listrik Pada Sosial Media X Gathot Hanyokro Kusuma; Inggih Permana; Febi Nur Salisah; M. Afdal; Muhammad Jazman; Arif Marsal
JUSIFO : Jurnal Sistem Informasi Vol 9 No 2 (2023): December
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v9i2.21354

Abstract

Environmental issues and the depletion of fossil fuels continue to escalate as the number of fossil fuel-based vehicle users increases in Indonesia. Electric vehicles emerge as one of the potential alternative solutions to address current environmental challenges, given their eco-friendly nature and lack of pollution emissions. Sentiment analysis is conducted to understand public responses, both supportive and opposing, towards electric vehicles. This research aims to analyze the sentiment of X-social media users regarding electric vehicles using machine learning techniques. The research stages include data collection, data selection, preprocessing, and classification using Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms. The test results show that on a balanced dataset using ROS, SVM performs the best with accuracy = 68.7%, precision = 77.9%, and recall = 68.4%. Meanwhile, NBC yields an accuracy of 60.3%, precision of 61.3%, and recall of 60.3%, while KNN has an accuracy of 53.9%, precision of 54%, and recall of 53.9%.
Perbandingan Algoritma KNN, NBC, dan SVM: Analisis Sentimen Masyarakat Terhadap Perparkiran di Kota Pekanbaru Sofia Fulvi Intan; Inggih Permana; Febi Nur Salisah; M. Afdal; Fitriani Muttakin
JUSIFO : Jurnal Sistem Informasi Vol 9 No 2 (2023): December
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v9i2.21357

Abstract

The public response in Pekanbaru to parking policies and regulations has given rise to various sentiments, both positive and negative. This discussion extends not only within the local community but also across various social media platforms. This research aims to analyze public sentiment towards the new parking policies and regulations in the Pekanbaru area. The study involves the KNN, NBC, and SVM algorithms to classify public sentiment into positive, neutral, and negative categories. Balancing techniques used in this research include Random Over Sampling (ROS) and Random Under Sampling (RUS). The data utilized in this study were obtained from posts on the social media platform X. The testing of the dataset using ROS resulted in high accuracy, precision, and recall values. The findings of this research indicate that overall, the SVM algorithm outperforms KNN and NBC in terms of accuracy, precision, and recall. Additionally, the most dominant sentiment is negative, with 422 tweets expressing dissatisfaction with the current parking policies.
PERANCANGAN ARSITEKTUR SISTEM INFORMASI ENTERPRISE MENGGUNAKAN TOGAF PADA MA’HAD AL HUSNA INDRAGIRI Najmuddin Mubarak MR; Febi Nur Salisah; Megawati Megawati; Fitriani Muttakin
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5828

Abstract

Ma’had Al Husna Indragiri merupakan pondok pesantren yang didirikan pada tahun 2019, mencakup dua sekolah yaitu SMP IT Al Husna Indragiri dan MA Al Husna Indragiri. Tujuan dari penelitian ini adalah untuk membuat kerangka kerja TOGAF untuk digunakan dalam desain arsitektur sistem informasi enterprise Ma'had Al Husna Indragiri serta mengidentifikasi dan menggambarkan arsitektur bisnisnya, mencakup Tata Usaha, Sarana Prasarana, Keuangan, Kegiatan Belajar Mengajar, Kesantrian, dan Pimpinan Ma'had. Melalui pemetaan proses dan diagram konsep solusi, penelitian ini memberikan gambaran menyeluruh mengenai interaksi antara berbagai komponen dalam Ma'had. Metode pengembangan sistem menggunakan pendekatan TOGAF, yang terdiri dari beberapa tahap: tahap awal, tahap A: Visi Arsitektur, tahap B: Arsitektur Bisnis, tahap C: Arsitektur Sistem Informasi, dan tahap D: Arsitektur Teknologi. Pimpinan Ma'had memainkan peran penting dalam pengawasan dan pengambilan keputusan strategis, Tata Usaha bertanggung jawab atas administrasi dan operasional harian, Sarana Prasarana memastikan ketersediaan dan kelayakan fasilitas fisik, Keuangan mengelola aspek finansial termasuk pemasukan, pengeluaran, penggajian dan pelaporan, KBM mengembangkan kurikulum, menyusun jadwal, dan melaksanakan proses belajar mengajar, serta Kesantrian menangani pendaftaran dan pengelolaan siswa. Penelitian ini mengidentifikasi beberapa masalah utama yang dihadapi Ma’had Al Husna Indragiri, seperti proses manual yang tidak efisien, kurangnya integrasi data, dan pengelolaan data yang tidak terstruktur. Berdasarkan analisis tersebut, disarankan untuk mengimplementasikan sistem informasi terintegrasi dan pengembangan basis data terpusat guna meningkatkan efisiensi operasional dan kualitas layanan pendidikan. Hasil penelitian yang didapatkan berupa blueprint arsitektur Ma'had Al Husna Indragiri yang diharapkan dapat berguna menjadi pedoman dalam melakukan pengembangan sistem informasi manajemen Ma'had Al Husna Indragiri
ANALISIS SENTIMEN ULASAN APLIKASI M-PASPOR MENGGUNAKAN NAIVE BAYES DAN SUPPORT VECTOR MACHINE Rahman Maheri; Febi Nur Salisah; Fitriani Muttakin; Megawati Megawati
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5826

Abstract

ABSTRAKM-Paspor merupakan sebuah aplikasi pelayanan publik yang dirilis pada awal tahun 2022 lalu. Telah lebih dari satu juta unduhan aplikasi ini di Google Play Store. Dalam beberapa kasus, konten ulasan dan ulasan produk atau layanan dapat berbeda. Analisis umpan balik pengguna harus menjadi prioritas utama saat mengembangkan aplikasi. Penelitian ini dilakukan dengan tujuan untuk memahami persepsi pengguna aplikasi M-Paspor di Google Play Store, dengan cara mengidentifikasi sentimen positif dan negatif dalam ulasan mereka, serta Membandingkan kemampuan algoritma Support Vector Machine dan Naive Bayes dalam menghasilkan klasifikasi yang akurat dalam menganalisis sentimen tersebut. Metode penelitian melibatkan pengumpulan, preprocessing, pelabelan, dan klasifikasi 5.934 ulasan aplikasi M-Paspor di Google Play Store, dan penerapan algoritma Support Vector Machine dan Naive Bayes untuk mengklasifikasikan sentimen ulasan. Setelah menerapkan algoritma, tahap terakhir adalah pengujian evaluasi. Hasil penerapan algoritma klasifikasi, menunjukkan bahwa algoritma Support Vector Machine dapat mengklasifikasikan ulasan aplikasi M-Paspor dengan lebih akurat, dengan rata-rata 80,76% dibandingkan dengan 78,12% dari Naive Bayes. Temuan ini menunjukkan potensi Support Vector Machine dalam membantu pengembang M-Paspor meningkatkan kualitas layanan berdasarkan umpan balik pengguna. ABSTRACTM-Paspor is a public service application that was released in early 2022. There have been more than one million downloads of this application on the Google Play Store. In some cases, review content and product or service reviews may differ. Analyzing user feedback should be a top priority when developing apps. This research was conducted with the aim of understanding the perceptions of users of the M-Paspor application on the Google Play Store, by identifying positive and negative sentiments in their reviews, and comparing the ability of the Support Vector Machine and Naive Bayes algorithms to produce accurate classifications in analyzing these sentiments. The research method involves collecting, preprocessing, labeling, and classifying 5,934 reviews of the M-Paspor app on the Google Play Store, and applying the Support Vector Machine and Naive Bayes algorithms to classify the sentiment of the reviews. After applying the algorithm, the last stage is evaluation testing. The results of applying the classification algorithms, show that the Support Vector Machine algorithm can classify M-Paspor app reviews more accurately, with an average of 80.76% compared to 78.12% from Naive Bayes. These findings demonstrate the potential of Support Vector Machine in helping M-Paspor developers improve service quality based on user feedback.
Development Disparity Analysis: Clustering Regencies and Cities in Riau Province Based on Socio-Economic Indicators Using K-Means and Hierarchical Algorithms Yogi Hariadi; Inggih Permana; Febi Nur Salisah; M. Afdal
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 2 (2026): September 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i2.1454

Abstract

Inter-regional socio-economic disparity remains a persistent challenge in regional development across Indonesia, including Riau Province. Differences in economic capacity, human development quality, poverty levels, and labor absorption indicate that development characteristics across regencies and cities are not yet uniform. This study aims to classify regencies and cities in Riau Province based on socio-economic indicators for the 2019–2023 period using K-Means and Hierarchical Clustering algorithms, as well as to compare the most suitable method for representing the data structure. Secondary data utilized in this study consist of five main macro-indicators: Gross Regional Domestic Product (GRDP) per capita, poverty rate, open unemployment rate, Human Development Index (HDI), and mean years of schooling. The research stages involve data preprocessing, Min-Max normalization, outlier detection, clustering algorithm implementation, and evaluation using the Silhouette Coefficient, Davies-Bouldin Index, and Calinski-Harabasz Score. The results demonstrate that the optimal number of clusters is two, where both algorithms yield identical evaluation values: a Silhouette Coefficient of 0.55, a Calinski-Harabasz Score of 34.45, and a Davies-Bouldin Index of 0.47. The clustering results reveal that Pekanbaru City forms a distinct, solitary cluster due to its superior macro-indicators, while the other eleven regencies and cities are grouped into the same cluster. This finding confirms a sharp socio-economic division between the provincial capital and its peripheral regions, which can simultaneously be utilized by the regional government as an empirical baseline evaluation to accelerate the achievement of SDGs Goal 1, Goal 4, and Goal 8 targets at the local level.
Co-Authors Arif Marsal Fitriani Muttakin Gathot Hanyokro Kusuma Inggih Permana M. Afdal Megawati Megawati Muhammad Jazman Najmuddin Mubarak MR Rahman Maheri Sofia Fulvi Intan Yogi Hariadi