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All Journal International Journal of Advances in Applied Sciences Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor JUITA : Jurnal Informatika Jurnal Informatika dan Teknik Elektro Terapan SMATIKA RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika JURNAL REKAYASA TEKNOLOGI INFORMASI SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Nasional Komputasi dan Teknologi Informasi Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal Mnemonic Jurnal ABDINUS : Jurnal Pengabdian Nusantara Jurnal Teknika Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Jurnal ABDIMAS Budi Darma Joutica : Journal of Informatic Unisla Journal of Artificial Intelligence and Engineering Applications (JAIEA) Seminar Nasional Ilmu Terapan Jurnal Abdimas Terapan Dedication : Jurnal Pengabdian Masyarakat Journal Of Artificial Intelligence And Software Engineering Jurnal ilmiah teknologi informasi Asia Smatika Jurnal : STIKI Informatika Jurnal Jurnal Inovasi Informatika dan Bisnis Digital (JIIBD)
Siti Mujilahwati
Teknik Informatika, Fakultas Teknik, Universitas Islam Lamongan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Other

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Pengenalan teknologi pembelajaran translanguaging berbasis web untuk meningkatkan literasi bahasa Arab-Indonesia di SMA Muhammadiyah 1 Babat Danang Bagus Reknadi; Siti Mujilahwati; Sugeng Dwi Hartantyo; M. Ghofar Rohman; Sholihul Amri; Uzlifatul Masruroh Isnawati
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 6 (2025): November
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v9i6.35722

Abstract

AbstrakKegiatan pengabdian kepada masyarakat ini bertujuan untuk mengenalkan dan mengimplementasikan teknologi translanguaging berbasis web sebagai sarana pendukung peningkatan literasi pembelajaran Bahasa Arab-Indonesia bagi siswa. Media pembelajaran yang tersedia sebelumnya masih terbatas dan belum mampu mengintegrasikan bahasa Arab dan bahasa Indonesia dalam satu platform, sehingga siswa mengalami kesulitan memahami materi secara lebih kontekstual. Kegiatan ini dilaksanakan di SMA Muhammadiyah 1 Babat, dengan melibatkan guru Bahasa Arab dan siswa sebagai peserta uji coba. Proses pengabdian dilakukan melalui analisis kebutuhan, perancangan aplikasi, pengembangan berbasis web, dan uji coba lapangan. Data diperoleh melalui penyebaran angket kepuasan serta observasi langsung terhadap aktivitas belajar mengajar. Tingkat kepuasan guru dan siswa diukur menggunakan kuesioner dengan skala penilaian terhadap kemudahan penggunaan, tampilan, dan manfaat aplikasi dalam proses pembelajaran. Aplikasi translanguaging ini dilengkapi fitur terjemahan kontekstual dua arah, kamus interaktif, dan latihan pemahaman teks yang membantu siswa meningkatkan literasi bahasa serta memudahkan guru dalam penyampaian materi.. Hasil uji menunjukkan tingkat kepuasan guru sebesar 87% dan siswa sebesar 90%, yang menandakan aplikasi ini diterima dengan baik. Secara keseluruhan, kegiatan ini membuktikan bahwa teknologi translanguaging berbasis web efektif mendukung literasi pembelajaran Bahasa Arab-Indonesia, dengan ruang lingkup yang masih dapat diperluas pada jenjang pendidikan lain agar manfaatnya semakin luas. Kata kunci: translanguaging; literasi; berbasis web; bahasa Arab-Indonesia; teknologi pembelajaran. AbstractThis community service activity aims to introduce and implement web-based translanguaging technology as a means of supporting students' Arabic-Indonesian language learning literacy. Previously available learning media were limited and unable to integrate Arabic and Indonesian into a single platform, resulting in students having difficulty understanding the material more contextually. This activity was carried out at SMA Muhammadiyah 1 Babat, involving Arabic language teachers and students as trial participants. The community service process was carried out through needs analysis, application design, web-based development, and field trials. Data were obtained through the distribution of satisfaction questionnaires and direct observation of teaching and learning activities. Teacher and student satisfaction levels were measured using questionnaires with a rating scale for ease of use, appearance, and the application's usefulness in the learning process. This translanguaging application is equipped with a two-way contextual translation feature, an interactive dictionary, and text comprehension exercises that help students improve language literacy and facilitate teachers in delivering material. The test results showed a teacher satisfaction level of 87% and a student satisfaction level of 90%, indicating that the application was well received. Overall, this activity proves that web-based translanguaging technology effectively supports Arabic-Indonesian language learning literacy, with a scope that can still be covered at other levels of education so that its benefits are even broader. Keywords: translanguaging; literacy; web-based; Arabic-Indonesian; learning technology.
Analisis Algoritma Naive Bayes Pada Penerimaan CPNS (Studi Kasus : Kementerian Hukum Jawa Timur 2024 Penjaga Tahanan) Seroja, Aulia Nadia Bunga; Mujilahwati, Siti; Bettaliyah, Azza Abidatin
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 9, No 4 (2025): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v9i4.22042

Abstract

Pengadaan Calon Pegawai Negeri Sipil (CPNS) Kementerian Hukum Jawa Timur tahun 2024 formasi penjaga tahanan menyediakan kuota 252 laki-laki dan 108 perempuan. Dengan jumlah pendaftar yang sangat besar, proses seleksi harus dilakukan secara ketat untuk memastikan terpilihnya calon ASN yang kompeten. Penerapan algoritma Naive Bayes untuk melakukan prediksi penerimaan peserta CPNS, bertujuan untuk menganalisis kinerjanya pada formasi penjaga tahanan tahun 2024 dan memperoleh tingkat akurasi tinggi. Naive Bayes dipilih karena kemampuan klasifikasi yang baik dan proses perhitungannya yang efisien. Data yang digunakan berjumlah 150, dengan 7 atribut : nilai TWK, TIU, TKP, CAT, kesehatan dan pengamatan fisik, kesamaptaan dan keterampilan, serta wawancara. Analisis dilakukan dengan perbandingan data latih dan data uji ke dalam tiga scenario yaitu 90:10, 80:20, dan 70:30. Hasil evaluasi memperlihatkan analisis pertama menghasilkan akurasi 93,33%, precision 83,33%, recall 100%, dan f1-score 90,90%. Analisis kedua dan ketiga mencapai nilai sempurna 100% untuk seluruh metrik. Hasil menunjukkan bahwa algoritma Naive Bayes sangat tepat dan pantas digunakan untuk memprediksi penerimaan CPNS, khususnya pada formasi penjaga tahanan di Kementerian Hukum Jawa Timur.
Analisis Konten Trash Talking dalam Konten Game Online Mobile Legends pada Akun YouTube Jonathan Liandi menggunakan Metode KNN Ahmad Dwi Raharjo Septiawan; Siti Mujilahwati; Azza Abidatin Bettaliyah
Jurnal Inovasi Informatika dan Bisnis Digital (JIIBD) Vol 1 No 1 (2025): November 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jiibd.v1i1.1821

Abstract

Fenomena trash talking (ujaran provokatif) dalam konten Mobile Legends: Bang Bang di YouTube dapat memengaruhi pola komunikasi di kalangan audiens muda, khususnya remaja. Penelitian ini bertujuan untuk menganalisis bentuk linguistik trash talking pada kanal Jonathan Liandi, mengembangkan pengklasifikasi otomatis untuk komentar trash talking, dan menguji potensi dampaknya terhadap penonton. Korpus sebanyak 500 komentar YouTube dikumpulkan melalui web scraping dan diproses awal menggunakan case folding, cleansing, normalisasi, tokenisasi, penghapusan stopword (stopword removal), dan stemming. Fitur diekstraksi menggunakan metode Term Frequency–Inverse Document Frequency (TF–IDF) dan diklasifikasikan dengan algoritma k-Nearest Neighbor (KNN) dengan nilai k=3. Dataset dibagi menjadi 80% untuk pelatihan (training) dan 20% untuk pengujian (testing), serta kinerja model dievaluasi menggunakan akurasi, presisi, recall, dan F1−score. Temuan mengungkapkan bahwa trash talking muncul dalam delapan kategori linguistik, termasuk istilah hewan, bagian tubuh, kata sifat, kata benda, kata kerja, kondisi atau keadaan, profesi atau status sosial, dan makian spontan (spontaneous expletives). Model KNN mencapai akurasi 85%, presisi 93,5%, recall 78,2%, dan F1−score 85,1%, yang menunjukkan bahwa TF–IDF yang dikombinasikan dengan KNN menyediakan garis dasar (baseline) yang efektif untuk mendeteksi bahasa ofensif dalam komentar terkait game. Trash talking dalam konten yang dianalisis bervariasi dan sangat terlihat di kalangan audiens. Meskipun pendekatan komputasi ini terbukti cocok untuk deteksi tahap awal, integrasi model yang peka konteks seperti deep learning serta promosi literasi digital yang lebih kuat dan kebijakan pembatasan usia disarankan untuk memitigasi dampak perilaku negatif.
Sentiment Analysis of Film Audience for IPAR ADALAH MAUT Using Support Vector Machine Surya Agung Agan Saputra; Siti Mujilahwati; Azza Abidatin Bettaliyah
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2266

Abstract

This study aims to analyze user sentiment on social media X (formerly Twitter) toward the film Ipar Adalah Maut using the Support Vector Machine (SVM) method. The data were collected through a crawling process using the snscrape library, focusing on tweets containing keywords related to the film title. The preprocessing stages included data cleaning, case folding, tokenization, stopword removal, and stemming, while feature extraction was performed using Term Frequency Inverse Document Frequency (TF-IDF). Sentiment was classified into two categories, namely positive and negative, using the SVM algorithm. The results showed that the model achieved 100% accuracy on the training data and 82% accuracy on the testing data, indicating good generalization performance, although there is a potential risk of overfitting due to the gap between training and testing results. These findings demonstrate the effectiveness of SVM in analyzing sentiment related to film discussions on social media and provide a basis for future research by incorporating larger and more balanced datasets.
PENINGKATAN PEREKONOMIAN UMKM MELALUI PENGEMBANGAN SISTEM INFORMASI WISATA PANTAI PENGKOLAN DI DESA KANDANGSEMANGKON Siti Mujilahwati; Miftahus Sholihin; M.Rosidi Zamroni; Muhammad Nur Fikri Alfarisi; Muhammad Alvin Firdaus; Qonit Zirby; Muhammad Rayendra AlMuhibbi; Moh Adam Mufrody; Febrian Abie Prsatama; M Chabib Nurroziqin; Achmad Nurasel Farizki
Jurnal Abdimas Terapan Vol. 4 No. 1 (2024): JURNAL ABDIMAS TERAPAN (NOVEMBER)
Publisher : Program Vokasi Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56190/jat.v4i1.66

Abstract

Kandangsemangkon Village, situated on the northern coast of Lamongan Regency, exhibits considerable potential for tourism development, largely due to its proximity to the notable Pengkolan Beach. However, the absence of effective promotional strategies and a lack of visibility represent a significant challenge in the development of tourism and the empowerment of local micro, small, and medium-sized enterprises (MSMEs). The objective of this community service program is to enhance the visibility of Pengkolan Beach and bolster the local economy through the establishment of a comprehensive tourism information system website. A series of activities, including field surveys, website development, training for MSMEs, and digital promotion, has enabled the creation of an effective platform for introducing the tourism potential and local products of the area to a wider audience. While there is no specific measurement regarding visibility, the positive response from the community and MSME actors indicates that this website has had a significant impact. The results of this program demonstrate an increase in the promotion of tourist destinations and sales of MSME products. To ensure the sustainability of this program, it is essential to prioritize website maintenance, continued training, and the enhancement of tourism infrastructure. Consequently, Kandangsemangkon Village can continue to develop as a renowned tourist destination, which will ultimately reinforce the local economy and community welfare.
ANALISIS SENTIMEN PENGGUNA APLIKASI CHATGPT BERDASARKAN RATING MENGGUNAKAN METODE LEXICON Siti Mujilahwati
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 9 No 1 (2024): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v9i1.3845

Abstract

The study aims to evaluate the feelings of ChatGPT users based on the given rating, using lexicon methods to classify feelings in user comments without requiring prior class labels. This lexicon method uses the vader lexicon dictionary to calculate the sentimental polarity of words in comments. The comment data set is obtained from an external source, namely the Kaggle data set, which did not have a previous sentiment label. The lexicon model is applied as an unsupervised learning approach to understanding the expression of user sentiment. The comments in the dataset are processed and analyzed using a lexicon model. The results of this sentimental analysis show that the lexicon method produces comments that tend to be positive or negative for the rating range one to five. Nonetheless, research findings suggest that some aspects of sentiment may not be accurately detected by this method. Nevertheless, this sentiment analysis provides an important basis for further understanding of the interaction between users and applications. These results can be the basis for more in-depth research involving more sophisticated approaches to sentiment analysis.
KLASIFIKASI SENTIMEN KOMENTAR TWITTER TERHADAP PROGRAM MAKAN BERGIZI GRATIS MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM) KHOIRIYATUL MAGHFIROH; Siti Mujilahwati
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7930

Abstract

Thei Freiei Nutritious Meial Program (MBG), which is deisignateid as onei of thei goveirnmeint’s strateigic policieis, has eiliciteid a widei rangei of public reisponseis, particularly on Twitteir (X). This condition neiceissitateis thei application of a data-drivein approach to eixaminei thei dynamics of public seintimeint as a foundation for policy eivaluation. This study is focuseid on thei deisign and impleimeintation of an LSTM modeil to ideintify seintimeint in public commeints, whilei its peirformancei is eivaluateid using accuracy, preicision, reicall, and F1-scorei meitrics. Thei data analyzeid consisteid of 3,459 commeints obtaineid from Kagglei, which weirei subseiqueintly proceisseid through seiveiral preiproceissing stageis. Thei annotation proceiss was conducteid using a leixicon-baseid approach with thei InSeit Leixicon dictionary, whilei teixt reipreiseintation was constructeid through thei Word2Veic eimbeidding teichniquei. Furtheirmorei, thei dataseit was divideid into training and teisting seits with a proportion of 80:20. Thei labeil distribution showeid a dominancei of positivei seintimeint at 67.0%, followeid by neigativei seintimeint at 23.7% and strongly neigativei seintimeint at 9.3%, reifleicting a teindeincy of public support accompanieid by criticism. Thei LSTM modeil, which was traineid using a configuration of 4 eipochs and a batch sizei of 32, deimonstrateid eixceilleint peirformancei, achieiving an accuracy of 93%, with preicision, reicall, and F1-scorei valueis eiach reiaching 0.93. Theisei reisults indicatei that thei deiveilopeid modeil posseisseis reiliablei classification capability and is suitablei to bei utilizeid as an analytical approach for systeimatically undeirstanding public opinion, theireiby contributing to data-drivein policy eivaluation and deicision-making.
ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP KEBIJAKAN PENCAMPURAN ETANOL PADA BBM MENGGUNAKAN METODE NAIVE BAYES Indah Ati; Siti Mujilahwati
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8283

Abstract

The Indonesian government's policy on ethanol blending in fuel has triggered public reactions on Twitter (X), particularly complaints about engine sputtering. This study analyzes public sentiment toward the ethanol fuel policy using the Naïve Bayes algorithm with TF-IDF feature extraction. Data were scraped using Apify with five keywords from October 2025 to May 2026, yielding 3,268 raw tweets reduced to 2,137 clean data points. Preprocessing comprised six stages: cleaning, case folding, slangword normalization, tokenizing, stopword filtering, and stemming using the Sastrawi library. Sentiment labeling used an Indonesian Lexicon method, and data were split 80:20 via Stratified Sampling into 1,709 training and 428 testing samples. Labeling showed a dominance of negative sentiment at 59.1% (1,262 data), followed by positive sentiment at 33.8% (723 data) and neutral sentiment at 7.1% (152 data). The Naïve Bayes model achieved an overall accuracy of 75.5%, with the best performance on the negative class (recall 91.3%, F1-Score 82.4%), moderate performance on the positive class (F1-Score 69.2%), but very poor performance on the neutral class (recall 3.3%, F1-Score 6.2%) due to extreme class imbalance. The study concludes that public opinion is predominantly negative, and that handling imbalanced data remains the primary priority for future model development.
Hybrid deep learning approach for Indonesian hoax detection: a comparative evaluation with IndoBERT Siti Mujilahwati; Moh. Rosidi Zamroni; Miftahus Sholihin
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp322-332

Abstract

The spread of hoaxes in Indonesia has escalated significantly, with over 12,547 cases recorded between 2018 and 2023. Low public literacy and uncontrolled information flow contribute to the rapid dissemination of false content that fuels disinformation and social unrest. Previous studies have utilized artificial intelligence (AI) approaches such as Indonesia bidirectional encoder representations from Transformers (IndoBERT) and deep learning models like long short-term memory (LSTM), bidirectional LSTM (BiLSTM), convolutional neural network (CNN), and Transformer-based methods. However, most relied on a single modeling paradigm and did not address the trade-offs between classification performance and computational efficiency. This study proposes a hybrid architecture that integrates IndoBERT with bidirectional gated recurrent unit (BiGRU) and BiLSTM to enhance Indonesian hoax detection. Using 4,312 news articles and 10-fold cross-validation, we compare the performance of IndoBERT–BiGRU, IndoBERT–BiLSTM, and the proposed hybrid IndoBERT–BiGRU BiLSTM model. Evaluation metrics include accuracy, precision, recall, F1 score, and training time. The hybrid model achieved the best performance with 98.73% accuracy, 99.01% recall, 98.04% precision, and 98.98% F1 score, while also reducing training time compared to single models. These findings demonstrate that combining BiGRU and BiLSTM within the IndoBERT framework effectively balances performance and efficiency, making it a robust solution for Indonesian text classification.
Evaluating Hybrid GA-SVM Feature Selection for Indonesian Sentiment Classification Using LSTM Siti Mujilahwati; Noor Zuraidin bin Mohd Safar
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.29602

Abstract

The high dimensionality and noisy characteristics of Indonesian social media text present significant challenges for sentiment classification models. Redundant and irrelevant features may reduce classification efficiency and negatively affect model generalization performance. This study evaluates a hybrid wrapper-based feature selection approach that integrates Genetic Algorithm (GA) and Support Vector Machine (SVM) to optimize TF-IDF feature representations before classification using Long Short-Term Memory (LSTM). The experiments were conducted on 1,918 Indonesian Twitter comments related to SARS-CoV-2 sentiment, consisting of 1,044 negative and 874 positive labels. The proposed GA-SVM mechanism reduced the feature space from 35,343 to 17,931 selected features. Two evaluation scenarios were employed in this study. Under the hold-out train-test split evaluation, the GA-SVM+LSTM model achieved the best accuracy of 91.41% using a learning rate of 0.0001. Meanwhile, the 10-fold cross-validation evaluation produced an average accuracy of 89.41%, indicating stable generalization performance across different data partitions. The experimental results also show that the proposed feature selection approach improved computational efficiency by reducing training time from 263.64 seconds to 173.54 seconds. Overall, the findings indicate that hybrid GA-SVM feature selection can effectively improve TF-IDF-based sentiment classification performance for Indonesian social media text.
Co-Authors Achmad Nurasel Farizki Agung Wahyudi Agustian, M. Afan AH KHAFID NASRUL UMAM umam Ahmad Dwi Raharjo Septiawan Aliyudin, Muhammad Amri, Sholihul Awaliyah, Bintan Udiyarini Azarki Ubaydillah, Muhammad Azza Abidatin Bettaliyah Azza Abidatin Bettaliyah AZZA ABIDATIN BETTALIYAH Bettaliyah, Azza Abidatin Danang Bagus Reknadi Danang Bagus Reknandi Dimas Lugiantoro Suko Dinar Mahdalena Leksana 1 Endang Setyati Erry Anggraini ERRY ANGGRAINI Febrian Abie Prsatama Fitri Rohmawati Hamid, Rahayu A Indah Ati Kevin Aringgi Salim KHOIRIYATUL MAGHFIROH M Ari Zidan Syafrizal M Chabib Nurroziqin M. Ali Na'im M. Ghofar Rohman M. Rosidi Zamroni M. ZAKI QOMARUDDIN M.Rosidi Zamroni Masruroh Masruroh Miftahus Sholihin Miftahus Sholihin Moh Adam Mufrody Muhammad Alvin Firdaus Muhammad Nur Fikri Alfarisi Muhammad Rayendra AlMuhibbi Nafiiyah, Nur Nawafilah, Nur Qomariyah Noor Zuraidin bin Mohd Safar Nur Nafi'iyah Nur nafi'iyah Nur Nawafilah Putra Samudra Qonit Zirby Rakhmawati, Eka Retno Wardhani Rizki Ardhian Ahmad Rizki Iwan Laksana Robiatul Adawiyah Salim, Kevin Aringgi Seroja, Aulia Nadia Bunga Suci Nur Fauziah Sugeng Dwi Hartantyo Surya Agung Agan Saputra Syamsul Arifin Tri Setyo Utomo