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All Journal International Journal of Advances in Applied Sciences Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor 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
Siti Mujilahwati
Teknik Informatika, Fakultas Teknik, Universitas Islam Lamongan
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Backpropagation untuk Memprediksi Jumlah Wisatawan Mancanegara ke Indonesia Kevin Aringgi Salim; Nur Nafi'iyah; Siti Mujilahwati
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 11 No 02 (2021): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v11i02.622

Abstract

Developing areas that have tourism potential is an effort to increase sources of income for villagers. Areas that have tourist areas can be a vehicle that attracts the attention of the public, both domestically and abroad. Tourists who come can provide income for tourist areas or the community. Therefore, predicting the number of incoming tourists can be predicted based on data from previous years. The goal is to make predictions to improve infrastructure and all needs for tourists. The purpose of this study is to apply the Backpropagation method to predict the number of foreign tourist visits to Indonesia. The dataset used in this study is 6000 lines and is divided into 4800 lines of training data, and 1200 lines of test data. The dataset is taken from the bps website, with the input variables being month, year, country of origin, tourist entrance to Indonesia, and the output variable being the number of tourists. The model of Backpropagation is evaluated by calculating MAE, and the architecture built is 4-9-1, 4 input layer nodes, 9 hidden layer nodes, and 1 output layer node. The test results of the MAE value of the Backpropagation method in predicting the number of tourists to Indonesia are 0.247.
Klasterisasi Kesehatan Gizi Bayi dan Balita Dengan Menggunakan Metode K-Means (Case Study : Kec. Deket Lamongan) Awaliyah, Bintan Udiyarini; 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.v9i4.22316

Abstract

Klasterisasi kesehatan gizi bayi dan balita di Kecamatan Deket menggunakan metode K-Means. Kesehatan gizi anak merupakan indikator penting dalam menentukan kualitas kesehatan masyarakat. Penelitian ini bertujuan untuk mengidentifikasi tingkat gizi bayi dan balita serta mempermudah pengelompokan data kesehatan yang diperoleh dari Puskesmas dan Posyandu. Metode K-Means dipilih karena kesederhanaan dan efisiensinya dalam mengelompokkan data. Data yang digunakan mencakup 3 atribut yaitu Stunting, Wasting, dan Underweight dari data kecamatan Deket dikumpulkan secara resmi pada tahun 2023. Proses penelitian melibatkan beberapa tahap, termasuk pengumpulan, pembersihan, dan analisis data untuk memastikan kualitas informasi yang optimal. Dengan menggunakan metode K-Means, penelitian ini menghasilkan tiga kategori utama status gizi, yaitu gizi butuk rendah, gizi buruk sedang, dan gizi buruk tinggi. Hasil klasterisasi diharapkan dapat memberikan informasi yang berguna bagi tenaga kesehatan dan pengambil keputusan dalam merumuskan kebijakan intervensi yang lebih efektif. Dengan adanya pemetaan status gizi yang jelas, diharapkan dapat meningkatkan perhatian terhadap kesehatan gizi anak di Kecamatan Deket. Penelitian ini juga menyarankan pentingnya kolaborasi antara Puskesmas, Posyandu, dan masyarakat dalam upaya meningkatkan kesehatan gizi balita. Melalui implementasi yang tepat, diharapkan masalah gizi buruk dapat diatasi secara efektif demi masa depan anak-anak yang lebih sehat dan berkualitas.
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.
Hybrid deep learning approach for Indonesian hoax detection: a comparative evaluation with IndoBERT Mujilahwati, Siti; Zamroni, Moh. Rosidi; Sholihin, Miftahus
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.
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.
Co-Authors Agung Wahyudi Agustian, M. Afan Alfarisi, Muhammad Nur Fikri Aliyudin, Muhammad AlMuhibbi, Muhammad Rayendra Amri, Sholihul Awaliyah, Bintan Udiyarini Azarki Ubaydillah, Muhammad Azza Abidatin Bettaliyah AZZA ABIDATIN BETTALIYAH Bettaliyah, Azza Abidatin Danang Bagus Reknadi Dimas Lugiantoro Suko Dinar Mahdalena Leksana 1 Endang Setyati Erry Anggraini ERRY ANGGRAINI Farizki, Achmad Nurasel Firdaus, Muhammad Alvin Fitri Rohmawati Hamid, Rahayu A Kevin Aringgi Salim M Ari Zidan Syafrizal M. Ali Na'im M. Ghofar Rohman M. Rosidi Zamroni M. ZAKI QOMARUDDIN Masruroh Masruroh Miftahus Sholihin Mufrody, Moh Adam Nafiiyah, Nur Nawafilah, Nur Qomariyah Nur Nafi'iyah Nur nafi'iyah Nur Nawafilah Nurroziqin, M Chabib Prsatama, Febrian Abie Putra Samudra 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 Zirby, Qonit