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All Journal International Journal of Artificial Intelligence Research Jurnal Ilmu Hukum, Humaniora dan Politik (JIHHP) Eduvest - Journal of Universal Studies
Aqqad, Muslihul
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Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Health Professions Law, Crime, Criminology & Criminal Justice Neuroscience Social Sciences

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Improving Performance Sentiment Analysis Movie Review Film using Random Forest with Feature Selection Information Gain Adiguna, Vinsent Brilian; Aqqad, Muslihul; Purwanto, Purwanto; Jaluanto Sunu, Jaluanto Sunu; Honorata Ratnawati, Honorata Ratnawati
International Journal of Artificial Intelligence Research Vol 8, No 1.1 (2024)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1.1227

Abstract

Sentiment analysis in film reviews is an important task to understand the audience's opinion towards a cinematic work. However, the complexity and subjectivity of language in film reviews pose a challenge. This research explores the application of Random Forest algorithm, an ensemble learning method, to perform sentiment classification on film reviews. Random Forest is built from a set of decision trees, each of which provides a prediction, and the final result is obtained from majority voting. This approach has the advantage of handling overfitting data. This research uses 500 review datasets along with positive and negative sentiment labels. The review text is represented as Information Gain and TF-IDF features to model the weight of each word. The Random Forest model is then trained using these features to predict sentiment labels. The performance of the model is evaluated using metrics such as accuracy, precision, recall and f1-score. The experimental results show that Random Forest is able to achieve 95.20% accuracy in sentiment classification of film reviews, surpassing the Support Vector Machine classification algorithm which in previous studies only achieved 92%. These findings provide a new perspective on the benefits of ensemble learning in sentiment analysis and its potential application in other domains such as marketing and public opinion analysis.
Classification of Banjarese Hulu and Kuala Dialects in Banjarese Prose Texts Aqqad, Muslihul; Rijati, Nova
Eduvest - Journal of Universal Studies Vol. 4 No. 10 (2024): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v4i10.39017

Abstract

This research focuses on classifying the Hulu and Kuala Banjarese dialects in the prose text “Datu Kandangan and Datu Kartamina”. These dialects represent linguistic variations resulting from geographical, social, and cultural differences among language communities, particularly in South Kalimantan, Indonesia. Language analysis methods such as Python Natural Language Toolkit (NLTK), NumPy, and Latent Dirichlet Allocation (LDA) Visualization (LyLDAvis) were employed to classify the dialects, involving data preprocessing steps like tokenization, punctuation removal, stop word normalization, and stemming. The research findings reveal the superiority of the "Naive Bayes" method over the "Boolean Query," achieving high accuracy in identifying positive examples and classifying texts into Upper and Lower Banjar dialects. The "Naive Bayes" method outperforms the "Boolean Query" with precision and recall values of 0.955563 and 0.956098, while the "Boolean Query" only reaches 0.021416 and 0.146341. This study makes a significant scholarly contribution to understanding language and cultural diversity in South Kalimantan, opening opportunities for further exploration in developing Natural Language Processing (NLP) technology for Indonesian regional languages.
Analisis Kepuasan Pelayanan Administrasi Publik di Bidang Pendidikan Studi Pengukuran Indeks Kepuasan Masyarakat di SMAN 3 Buntok Noorsalim, Mohd; Rulandari, Novianita; Aqqad, Muslihul
Jurnal Ilmu Hukum, Humaniora dan Politik Vol. 6 No. 2 (2026): (JIHHP) Jurnal Ilmu Hukum, Humaniora dan Politik
Publisher : Dinasti Review Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jihhp.v6i2.7832

Abstract

Penelitian ini mengkaji kualitas layanan administrasi publik di sektor pendidikan dengan mengukur kepuasan masyarakat di SMAN 3 Buntok. Sekolah tidak hanya berfungsi sebagai lembaga pendidikan tetapi juga sebagai penyedia layanan publik yang bertanggung jawab untuk memberikan layanan administrasi yang efisien dan transparan kepada siswa, orang tua, dan masyarakat. Sekolah juga pada masa ini menjadi ujung tombak implementasi kebijakan yang dilaksanakan pemerintah. Tidak secara langsung institusi sekolah menjadi Humas (Public Relation) pemerintah dalam memberikan informasi dan edukasi terhadap kebijakan publik  ke masyarakat sekitar sekolah. Evaluasi kualitas layanan sangat penting untuk memastikan akuntabilitas, daya tanggap, dan peningkatan berkelanjutan dalam manajemen pendidikan publik. Studi ini menyelidiki seberapa puas pengguna layanan dengan layanan administrasi di SMAN 3 Buntok dan mengidentifikasi dimensi layanan mana yang membutuhkan perbaikan. Penelitian ini menggunakan pendekatan deskriptif kuantitatif dengan desain cross-sectional survey. Data dikumpulkan dari siswa, orang tua, dan anggota masyarakat yang telah mengakses layanan administrasi di SMAN 3 Buntok dengan metode Random Sampling. Dimana semua anggota populasi bisa menjadi sampling. Menggunakan Kuesioner terstruktur berdasarkan Indeks Kepuasan Masyarakat (IKM) standar . Data dianalisis menggunakan statistik deskriptif dan prosedur penilaian IKM sesuai dengan pedoman evaluasi pelayanan publik. Pelayanan administrasi publik di SMAN 3 Buntok secara umum menunjukkan Indeks Kepuasan Masyarakat dengan predikat “Baik” dengan Nilai akhir 85,75. Hasil penelitian menunjukkan bahwa kepuasan layanan administrasi secara keseluruhan di SMAN 3 Buntok termasuk dalam kategori “baik”. Skor tertinggi tercatat untuk perilaku staf dan kejelasan layanan, sedangkan skor yang lebih rendah ditemukan pada waktu pemrosesan layanan dan penanganan pengaduan.  
Co-Authors Adiguna, Vinsent Brilian Honorata Ratnawati, Honorata Ratnawati Jaluanto Sunu, Jaluanto Sunu Noorsalim, Mohd Nova Rijati Novianita Rulandari Syamsiar, Syamsiar