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All Journal Jurnal Teknologi dan Manajemen Informatika Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Engagement: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Dewi Sartika
Universitas Sriwijaya
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Languange, Linguistic, Communication & Media Nursing Public Health Social Sciences

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Model Matematika Hibrida Lexicon–Harris Hawks Optimization untuk Analisis Sentimen Ulasan Produk Shopee Miftahul Falah; Yesinta Florensia; Dewi Sartika
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/h239eh92

Abstract

This research presents a hybrid Lexicon–Harris Hawks Optimization (HHO) mathematical model designed to improve sentiment analysis performance on Shopee product reviews. The rapid growth of e-commerce platforms has resulted in a large volume of user-generated content, making accurate sentiment classification essential for understanding customer opinions. Traditional lexicon-based methods are simple and interpretable but often limited by static sentiment scores that fail to capture contextual nuances in real-world review data. To overcome these limitations, this study integrates a lexicon-based scoring approach with HHO to dynamically optimize sentiment weights and enhance classification accuracy. The proposed method involves four main stages: data preprocessing, baseline lexicon-based sentiment scoring, lexicon weight optimization using HHO, and final sentiment classification. HHO is employed to search for optimal lexicon weight configurations through exploration and exploitation mechanisms modeled after the cooperative hunting behavior of Harris hawks. The optimized weights are then applied to recalculate sentiment scores and classify reviews into positive, negative, or neutral categories. Performance is evaluated using accuracy, precision, recall, and F1-score. Experimental results show that the hybrid model significantly outperforms the baseline lexicon method, achieving near-perfect classification performance. The confusion matrix reveals extremely low misclassification rates, while evaluation metrics exceed 90% across all categories. The convergence curve further demonstrates stable and efficient optimization behavior. Analysis of sentiment score outputs indicates improved sensitivity for both positive and negative expressions, as well as more accurate representation of reviewer intent.
Pengembangan Sistem Informasi Penomoran Surat Berbasis Web untuk Digitalisasi Administrasi Kelurahan Plaju Darat Bayu Wijaya Putra; Abdiansah Abdiansah; Sri Turatmiyah; Anna Dwi Marjusalinah; Dewi Sartika; Rusdi Efendi; Hasnan Afif; Muhammad Ali Buchari; Yesinta Florensia; Ezanovia Ezanovia; Aprillia Syafitri; Nabila Nabila; Lulu Usni Dwi Putri; Karen Nazzua Putri Pratami
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 6 No. 2 (2026): Maret 2026 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v6i2.1028

Abstract

Pengelolaan surat di Kelurahan Plaju Darat sebelumnya masih manual sehingga sering terjadi ketidakteraturan penomoran, keterlambatan pencarian arsip, dan rendahnya akurasi administrasi. Kegiatan pengabdian ini bertujuan menerapkan Sistem Informasi Penomoran Surat berbasis web untuk meningkatkan efisiensi dan akuntabilitas pelayanan. Pendekatan Participatory Action Research (PAR) digunakan melalui tahapan identifikasi masalah, analisis kebutuhan, pengembangan sistem, pengujian, pelatihan, dan evaluasi. Sistem dikembangkan menggunakan CodeIgniter dan MySQL, kemudian diuji dengan Black Box Testing serta User Acceptance Testing. Hasil pre-test menunjukkan rata-rata nilai 51 dan meningkat menjadi 86,13 pada post-test, atau peningkatan 68,88% setelah pelatihan. Evaluasi kepuasan pengguna menunjukkan skor sangat baik, berada pada rentang 4,35–4,65, dengan nilai tertinggi pada efisiensi pencarian arsip dan akurasi penomoran otomatis. Program ini berhasil meningkatkan kompetensi aparatur dan efektivitas administrasi, serta mendukung transformasi digital kelurahan.
Enhancing Teacher Competence in Coding Through Gamified Training: A Kirkpatrick Evaluation Samsuryadi Samsuryadi; Dewi Sartika; Meylani Utari; Kanda Januar Miraswan; Alvi Syahrini Utami; Mgs. Afriyan Firdaus; Osvari Arsalan
Engagement: Jurnal Pengabdian Kepada Masyarakat Vol. 10 No. 2 (2026): May 2026
Publisher : Asosiasi Dosen Pengembang Masyarajat (ADPEMAS) Forum Komunikasi Dosen Peneliti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29062/engagement.v10i2.2269

Abstract

Background: The Indonesian government, through the Ministry of Primary and Secondary Education (Kemendikdasmen), plans to implement coding and artificial intelligence learning programs in schools starting the 2025/2026 academic year. SMP Negeri 56 Palembang seeks to prepare by enhancing teacher competence in coding, as 56.8% of teachers initially had no basic coding knowledge. Purpose of the Study: This study aims to provide basic coding knowledge to teachers and develop skills in utilizing educational games (Blockly Game Maze) as interactive learning media that can be integrated into the learning process. Methods: Training and workshops were conducted involving 37 teachers from various subjects. A Kirkpatrick model evaluation was applied across four levels: reaction (participant satisfaction), learning (pre-test and post-test comparison), behavior (classroom observation during mentoring), and results (overall program effectiveness). Participants used e-modules and practiced with Blockly Game Maze educational games. Results: The reaction level showed a 94.59% satisfaction rate, indicating high relevance of training materials. The learning level demonstrated a 27.03% increase in coding competency, with participants previously unfamiliar with basic coding (56.8%) beginning to master basic logic and coding structures. At the behavior level, students in five groups successfully completed Blockly Game Maze up to level 6 (100%), with three groups completing level 7. These findings prove that the training not only provided theoretical insights but also successfully built teachers' confidence in adopting new technologies to support the learning process.
Comparative Analysis of SMOTE, WMOTE, and ADASYN Oversampling Methods on Multinomial Naive Bayes Performance in Classifying Toddlers Nutritional Status Naretha Kawadha Pasemah Gumay; Dewi Sartika; Rendra Gustriansyah; Yesinta Florensia; Miftahul Falah
Jurnal Teknologi dan Manajemen Informatika Vol. 12 No. 1 (2026): Juni 2026
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Class imbalance in toddler nutritional status data often reduces the ability of classification models, especially in predicting minority classes. This study aims to analyze the impact of three oversampling techniques, namely SMOTE, WMOTE, and ADASYN, on improving the performance of the Multinomial Naive Bayes (MNB) algorithm. A dataset of 243 data was processed through a preprocessing stage by converting categorical variables using numeric labels. To meet the MNB algorithm's requirement for non-negative data, continuous numeric features (such as birth weight, birth height, weight, height, and age) were normalized using the Min-Max Scaler to the range [0, 1]. This process discretizes continuous values onto a probability scale to ensure feature compatibility with the Multinomial distribution. Data balancing was performed only on the training dataset, where the SMOTE method produced 374 data, ADASYN produced 375 data, and WMOTE produced 373 data. The evaluation results show that although all three oversampling methods experienced a slight decrease in global accuracy, the model's ability to detect minority classes improved, as evidenced by increases in G-Means and Balanced Accuracy. The test results concluded that MNB-ADASYN was the best model for prioritizing high sensitivity to all class labels, while MNB-WMOTE provided the most consistent global accuracy stability while maintaining performance on minority classes.
Co-Authors Abdiansah, Abdiansah Alvi Syahrini Utami Anna Dwi Marjusalinah Aprillia Syafitri Bayu Wijaya Putra Buchari, Muhammad Ali Ezanovia Ezanovia Hasnan Afif Kanda Januar Miraswan Karen Nazzua Putri Pratami Lulu Usni Dwi Putri Meylani Utari Mgs Afriyan Firdaus Miftahul Falah Nabila Nabila Naretha Kawadha Pasemah Gumay Osvari Arsalan Rendra Gustriansyah Rusdi Efendi Samsuryadi Samsuryadi Sri Turatmiyah Yesinta Florensia