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All Journal JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi JMM (Jurnal Masyarakat Mandiri) SINTECH (Science and Information Technology) Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal ULTIMATICS JURIKOM (Jurnal Riset Komputer) Journal on Education JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Informatika Global Prosiding Seminar Nasional Multidisiplin Jurnal Sistem Informasi dan Informatika (SIMIKA) Vocatech : Vocational Education and Technology Journal Jurnal Sistem Komputer dan Informatika (JSON) At-Thullab : Jurnal Pendidikan Guru Madrasah Ibtidaiyah Jurnal Inovasi Penelitian Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian kepada Masyarakat Journal of Education Research Indonesian Community Journal Edu Research : Jurnal Penelitian Pendidikan Inspirasi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Media Bina Ilmiah Journal of Research Applications in Community Services AL-UMRON: Jurnal Pengabdian kepada Masyarakat E-Amal: Jurnal Pengabdian Kepada Masyarakat Almantiq NAWASENA BHAKTI Journal of Educational Research and Practice SAHABAT: Jurnal Pendidikan Anak Usia Dini
Ita Aristia Sa'ida
Universitas Nahdlatul Ulama Sunan Giri
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Peningkatan Perekonomian Masyarakat Desa Sambongrejo Melalui Produksi Makanan Berbahan Dasar Tahu Abidin, Muhammmad Zaenal; Sa’ida, Ita Aristia; Cholifah, Siti
Journal of Research Applications in Community Service Vol. 1 No. 1 (2022): Journal of Research Applications in Community Service
Publisher : Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/jarcoms.v1i1.880

Abstract

Desa Sambongrejo merupakan salah satu desa yang terletak di kecamatan Sumberrejo Kabupaten Bojonegoro. Di desa tersebut terdapat pabrik tahu yang dikelola secara tradisional. Tahu tersebut dijual secara langsung kepada para penjual lain ataupun konsumen lansung. Tahu yang diolah lebih lanjut tentunya memiliki nilai ekonomis lebih tinggi dan dapat meningkatan pendapatan masyarakat. Berdasarkan hasil analisa tersebut, kegiatan pengabdian kepada masyarakat ini adalah pemberian pelatihan produksi makanan berbahan baku tahu dan lomba memasak berbahan baku tahu. Sasaran kegiatan ini adalah ibu-ibu PKK dan kelompok pemuda-pemudi karang taruna Desa Sambongrejo. Metode pelaksanaan pengabdian adalah penyuluhan, pelatihan, perlombaan dan pelaporan. Kegiatan ini menghasilkan beberapa produk makanan berbahan dasar tahu di antaranyaadalah keripik tahu, lontong tahu, lumpia tahu dan rolade tahu.
Meningkatkan Ekonomi Melalui Usaha Keripik Tempe di Desa Bayemgede Kecamatan Kepohbaru Kabupaten Bojonegoro Tawakkal, M. Iqbal; Sa’ida, Ita Aristia; Huda, Nurul; Sholihah, Nurul Maratus
Journal of Research Applications in Community Service Vol. 2 No. 2 (2023): Journal of Research Applications in Community Service
Publisher : Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/jarcoms.v2i2.1394

Abstract

Desa Bayemgede terletak di ujung kabupaten kecamatan kepohbaru. Keberadaan lokasi tersebut, menjadikan desa Bayemgede lebih beragam dan memiliki potensi dalam berbagai bidang yakni segi pendidikan, ekonomi, keagamaan, dan kesehatan. Tujuan penelitian ini adalah untuk mengetahui kondisi bidang pendidikan, ekonomi, keagamaan, dan kesehatan. Metode dalam penelitian ini adalah melakukan observasi, analisis, dan mapping data. Hasil penelitian potensi dari bidang pendidikan, ekonomi, keagamaan, dan kesehatan mengalami peningkatan yang dibuktikan dengan etika perilaku masyarakat, menciptakan produk kripik tempe Bayemgede untuk UMKM, lebih relegius, dan lebih sadar akan hidup sehat. Kesimpulan dengan adanya KKN Unugiri Di Desa Bayemgede mengalami peningkatan dalam kehidupan masyarakat di berbagai bidang pendidikan, ekonomi, agama, dan kesehatan.
TRANSFORMASI PENDIDIKAN BERBASIS LITERASI DIGITAL: MENINGKATKAN EFEKTIVITAS PEMBELAJARAN DI BOJONEGORO Cindarbumi, Festian; Kurniawati, Naning; Saida, Ita Aristia
JMM (Jurnal Masyarakat Mandiri) Vol 10, No 1 (2026): Februari
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v10i1.36370

Abstract

Abstrak: Kegiatan pengabdian masyarakat ini dilatar belakangi oleh rendahnya pemanfaatan literasi digital dalam proses pembelajaran pada satuan pendidikan menengah. Tujuan kegiatan ini adalah meningkatkan efektivitas pembelajaran melalui penguatan kompetensi literasi digital pendidik dan tenaga kependidikan. Metode yang digunakan meliputi sosialisasi, pelatihan, dan workshop penggunaan media pembelajaran digital berbasis aplikasi Canva untuk e-learning. Peserta kegiatan terdiri atas pendidik dan tenaga kependidikan. Evaluasi kegiatan dilakukan melalui observasi dan kuesioner pretest–posttest untuk menilai peningkatan pemahaman serta penerapan hasil pelatihan. Hasil kegiatan menunjukkan adanya peningkatan pemahaman literasi digital sebesar 80% berdasarkan perbandingan skor pretest dan posttest peserta, serta peningkatan efektivitas pembelajaran baik secara daring maupun luring. Kegiatan ini berkontribusi dalam mendorong transformasi pembelajaran yang lebih interaktif, kreatif, dan adaptif terhadap perkembangan teknologi pendidikan.Abstract: This community service activity was motivated by the low utilization of digital literacy in the learning process at the secondary education level. The purpose of this activity was to enhance learning effectiveness through strengthening the digital literacy competencies of educators and educational staff. The methods employed included socialization, training, and workshops on the use of digital learning media based on the Canva application for e-learning. The participants consisted of educators and educational staff. The evaluation was conducted through observation and pretest–posttest questionnaires to assess improvements in understanding and the application of training outcomes. The results indicated an 80% increase in participants’ digital literacy understanding based on a comparison of pretest and posttest scores, as well as improved learning effectiveness in both online and offline settings. This activity contributes to promoting a more interactive, creative, and adaptive transformation of learning in response to developments in educational technology.
PENGARUH KOMPOSISI SPLIT DATA PADA AKURASI KLASIFIKASI PENDERITA DIABETES MENGGUNAKAN ALGORITMA MACHINE LEARNING Febby Refindha Aftha Harianto; Zakki Alawi; Ita Aristia Sa’ida
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i1.3663

Abstract

The increasing number of people with diabetes is an international health problem. To prevent diabetic complications, early diagnosis and accurate classification are essential. This study looks at how the composition of split data affects the classification performance of diabetics with machine learning algorithms such as Random Forest, Naive Bayes, and Support Vector Machine (SVM). The research data is taken from Bojonegoro Regency Hospital, which consists of 128 samples that have 10 main features. To ensure the data is ready for use, the research method goes through a preprocessing stage. Next, the data was divided into training and testing data with a ratio of 90:10, 80:20, 70:30, 60:40, and 50:50 respectively. Using confusion matrix, the algorithm is assessed for accuracy, precision, recall, and F1 score. In this study we focus on the accuracy values obtained and the results show that the proportion of data sharing affects the performance of the algorithm. Random Forest achieved 100% accuracy in some scenarios. This algorithm also proved to be the most effective in the classification of diabetics. In conclusion, algorithm selection and data split composition are very important for model performance optimization. These results are important for the development of more accurate and efficient Machine Learning-based diagnosis systems. Further research can consider larger datasets and additional algorithms for better results.
Perbandingan Metode Euclidean dan Manhattan Distance dalam Implementasi Algoritma K-Means dan K-Medoid pada Pengelompokkan Faktor Dominan Perceraian di Kabupaten Bojonegoro Salma, Elok Salma Nabila; Ifnu Wisma Dwi Prastya; Ita Aristia Sa’ida
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9520

Abstract

The divorce rate in Bojonegoro Regency continues to increase, driven by various social factors such as constant disputes, economic pressure, and household disharmony. Consequently, an analysis is required to map dominant and non-dominant factors more effectively. This study aims to group the factors causing divorce in Bojonegoro Regency for the 2021–2023 period and determine the most optimal clustering method. The research utilizes K-Means and K-Medoids algorithms with Euclidean and Manhattan distance metrics applied to both raw data and data normalized using the Min–Max Scaler, evaluated via the Silhouette Score. The results indicate that data normalization improves cluster quality, and K-Means with Manhattan distance on normalized data achieves the best performance, yielding a Silhouette Score of 0.849547. Cluster displacement analysis reveals that the grouping patterns remain relatively consistent across years, with "constant disputes" consistently emerging as the dominant factor, while other factors remain in the non-dominant cluster with similar patterns. This study demonstrates that K-Means with Manhattan distance on normalized data is more effective for clustering divorce factors. These findings can serve as a methodological foundation for the local government in formulating data-driven social policies and interventions.
Evaluasi Pengaruh RFE Terhadap Kinerja Random Forest dengan SVM pada Klasifikasi Kemiskinan Kabupaten/Kota Indonesia Shafa Kirana Aralia; Mula Agung Barata; Ita Aristia Sa'ida
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9527

Abstract

Poverty is a socio-economic issue that remains a concern in Indonesia, with differences in development characteristics between districts/cities causing wide variations in indicators that are intercorrelated. Feature redundancy and the existence of extreme values have the potential to reduce the generalization ability of classification models and reduce the interpretability of results. Therefore, an approach is needed that not only produces high accuracy but is also capable of identifying the most relevant indicators. Therefore, an approach is needed that not only produces high accuracy but is also capable of identifying the most relevant indicators. This study aims to evaluate the effect of Recursive Feature Elimination (RFE) on the performance of Support Vector Machine (SVM) and Random Forest in classifying the poverty status of districts/cities in Indonesia. The dataset used consists of 514 observations with two target classes, namely non-poor and poor. The preprocessing stage included data cleaning and outlier handling using the IQR capping method, then the data was divided into 80% training data and 20% test data. Testing was conducted on four scenarios: SVM, SVM+RFE, Random Forest, and Random Forest+RFE. Evaluation used a confusion matrix, accuracy, precision, recall, and F1-score. The results show that RFE does not change the accuracy of SVM (0.971), but improves the performance of Random Forest from 0.981 to 0.99 and improves the precision of the minority class. The Random Forest+RFE combination is the most effective and efficient configuration for regional poverty classification.
Analisis Sentimen Komentar iPhone 17 pada Platform YouTube Menggunakan IndoBERT dan Support Vector Machine MARATUS SHOLIHAH, SITI; Afril Efan Pajri; Ita Aristia Sa’ida
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 3 (2026): Maret 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i3.9507

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen komentar YouTube berbahasa Indonesia terkait iPhone 17 dengan membandingkan metode Support Vector Machine berbasis Term Frequency–Inverse Document Frequency dan model IndoBERT. Data diperoleh melalui proses crawling komentar pada kanal YouTube GadgetIn, kemudian diproses melalui tahapan pre-processing untuk mengurangi noise dan menormalkan teks. Pelabelan sentimen dilakukan secara otomatis menggunakan InSetLexicon dengan dua kelas, yaitu positif dan negatif. Dataset selanjutnya dibagi menggunakan teknik stratified split menjadi data latih, validasi, dan uji. Selain dua model utama, pendekatan ensemble IndoBERT–SVM diuji sebagai metode tambahan untuk menilai stabilitas performa klasifikasi. Evaluasi dilakukan menggunakan confusion matrix serta metrik Accuracy, Precision, Recall, dan F1-score. Hasil pengujian menunjukkan bahwa IndoBERT memperoleh performa terbaik dengan nilai Accuracy sebesar  92, 29%, diikuti oleh model ensemble sebesar 91,63%, dan Support Vector Machine sebesar 88,99%. Temuan ini mengindikasikan bahwa model berbasis transformer lebih efektif dalam memahami konteks bahasa informal pada komentar YouTube dibandingkan metode berbasis fitur tradisional. Dengan demikian, penelitian ini memberikan bukti empiris mengenai efektivitas pendekatan machine learning dan transformer dalam analisis sentimen media sosial berbahasa Indonesia.
Analisis Faktor Keberhasilan Penjualan Kerajinan Tangan menggunakan Decision Tree dengan Optimasi Grid Search Septiana, Nailus Saidah Anindia; Vikri, Muhammad Jauhar; Sa’ida, Ita Aristia
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9534

Abstract

This study is motivated by the limited ability of handicraft Micro, Small, and Medium Enterprises (MSMEs) to analyze the key factors influencing sales success on e-commerce platforms, despite the availability of historical transaction data. Previous studies generally applied classification algorithms without systematic hyperparameter optimization, potentially leading to suboptimal models and overfitting issues. To address this gap, this research proposes the implementation of a Decision Tree algorithm optimized using Grid search Cross-validation. The dataset was obtained from the Brazilian e-commerce platform (Olist Dataset), specifically the ‘artes’ category as a proxy for handicraft products, with an 80:20 split for training and testing data. The optimization process explored 576 parameter combinations to determine the best configuration. The optimized model achieved an accuracy of 97.61% with a simplified tree structure (max_depth=None), enhancing interpretability. Feature importance analysis product_height_cm as the most dominant factor (64.23%), followed by product_height_cm, product_width_cm, Freight_value, product_weight_g, and price. These findings demonstrate that the combination of Decision Tree and Grid search effectively produces an accurate and interpretable predictive model, providing strategic decision-making support for handicraft MSMEs in digital marketplaces.
Komparasi Algoritma Machine Learning untuk Deteksi Review Palsu dan Rekomendasi Pembelian Pada Platform Lazada Prabowo, Affan Agung; Barata, Mula Agung; Sa'ida, Ita Aristia
VOCATECH: Vocational Education and Technology Journal Vol 8, No 1 (2026): April
Publisher : Akademi Komunitas Negeri Aceh Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38038/vocatech.v8i1.307

Abstract

AbstractThe rapid growth of e-commerce has increased the potential for the emergence of fake reviews that can mislead consumers and reduce the credibility of online purchasing decisions. This study aims to evaluate the performance of several machine learning algorithms in distinguishing fake and genuine reviews, as well as to develop a purchase recommendation model that considers review authenticity. The dataset used consists of 2,644 product reviews from the Lazada platform, which were labeled using a rule-based approach, followed by text preprocessing, normalization, and feature extraction using TF-IDF. The classification methods applied include Support Vector Machine (SVM), Decision Tree, Random Forest, Naive Bayes, and C4.5. The results show that Random Forest and C4.5 achieved the highest accuracy of 99.81%, followed by Decision Tree (99.62%), SVM (98.30%), and Naive Bayes (93.01%). In addition, a purchase recommendation score was developed by combining rating, sentiment, helpfulness, and purchase status to classify products into recommended and not recommended categories. The findings indicate that most reviews identified as fake still result in positive recommendations, which may introduce bias in conventional recommendation systems. Therefore, integrating fake review detection with sentiment analysis and multi-criteria evaluation is essential to improve the reliability of recommendation systems in e-commerce platforms. AbstrakMaraknya perkembangan e-commerce meningkatkan potensi munculnya ulasan palsu yang dapat menyesatkan konsumen dan menurunkan kredibilitas dalam pengambilan keputusan pembelian secara daring. Penelitian ini bertujuan untuk mengevaluasi kinerja beberapa algoritma machine learning dalam membedakan ulasan palsu dan asli, serta mengembangkan model rekomendasi pembelian yang mempertimbangkan keaslian ulasan. Dataset yang digunakan terdiri dari 2.644 ulasan produk pada platform Lazada yang diberi label menggunakan pendekatan rule-based, kemudian melalui tahapan preprocessing teks, normalisasi, dan ekstraksi fitur menggunakan TF-IDF. Metode klasifikasi yang diterapkan meliputi Support Vector Machine, Decision Tree, Random Forest, Naive Bayes, dan C4.5. Hasil pengujian menunjukkan bahwa Random Forest dan C4.5 mencapai akurasi tertinggi sebesar 99,81%, diikuti oleh Decision Tree (99,62%), SVM (98,30%), dan Naive Bayes (93,01%). Selain itu, dikembangkan skor rekomendasi pembelian dengan menggabungkan rating, sentimen, tingkat helpful, dan status pembelian untuk mengelompokkan produk ke dalam kategori direkomendasikan dan tidak direkomendasikan. Temuan menunjukkan bahwa sebagian besar ulasan yang terdeteksi sebagai palsu tetap menghasilkan rekomendasi positif, sehingga berpotensi menimbulkan bias pada sistem rekomendasi konvensional. Oleh karena itu, integrasi deteksi ulasan palsu dengan analisis sentimen serta penilaian multi-kriteria menjadi penting untuk meningkatkan keandalan sistem rekomendasi pada platform e-commerce.  
Outdoor Learning through Scientific Excursions to Foster Exploratory Attitudes in Early Childhood Ita Aristia Sa'ida
SAHABAT: Jurnal Pendidikan Anak Usia Dini Vol. 1 No. 1 (2025): SAHABAT: Jurnal Pendidikan Anak Usia Dini
Publisher : CV. AGRAPANA MEDIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65789/sahabat.v1i1.91

Abstract

This study explores the implementation of scientific excursions as a strategy to foster exploratory attitudes in early childhood education. Children at the early developmental stage naturally exhibit curiosity and a desire to explore their surroundings, making hands-on learning experiences essential. Scientific excursions provide opportunities for children to engage with real-life phenomena outside the classroom, allowing them to observe, question, and interact directly with their environment. This qualitative study employs a literature review approach, analyzing previous research and practical applications of outdoor educational activities and exploratory learning. Findings indicate that such excursions enhance cognitive, social, emotional, and motor development, while also promoting critical thinking and problem-solving skills. Teachers play a crucial role in facilitating these experiences, guiding learning while allowing children autonomy in exploration. The study highlights the importance of integrating experiential learning into early childhood education to cultivate lifelong curiosity and an active learning mindset. Implementing scientific excursions systematically can improve the quality and effectiveness of early childhood learning programs.
Co-Authors Abdul Faqih Abidin, Muhammmad Zaenal Afril Efan Pajri Agus Afriliyanto agustian, rifan Ainu Zuhriyah Ainu Zuhriyah Amalia Nur Laily APRILLIA, YUSIFA Audytra, Hastie Ayu Mauliana Intahaya Bakti Putra Pamungkas Ceisa Santi, Nirma Devi Dwi Nuraliza Diah Anggraini, Diah Diah Fitriani Dirgantoro, Guruh Putro Dirgantoro, Guruh Putro Dirgantoro Dwi Issadari Hastuti Eka Asfira Eli Widyawati, Reza Elsa Azia Ulhaq Fajar Nawulansih, Dewi Febby Refindha Aftha Harianto Festian Cindarbumi Guruh Putro Dirgantoro Ifnu Wisma Dwi Prastya Jauhar Vikri, Muhammad Kurniawati, Naning M. Iqbal Tawakkal MARATUS SHOLIHAH, SITI Mochamad Nizar Palefi Ma’ady Muarofah, Isna Ayu Muhammad Adib Asrori Muhammad Zaenal Abidin Mula Agung Barata Nawafila Februyani Niken Sukmawati Nurul Huda Prabowo, Affan Agung Sahri Sahri Sahri Sahri Salma, Elok Salma Nabila Sanjaya, Ucta Pradema Santi, Nirma Ceisa Saputra, Agus Bima Septiana, Nailus Saidah Anindia Shafa Kirana Aralia Sholihah, Nurul Maratus SITI CHOLIFAH Siti Cholifah Siti Shoimah Sunu Wahyudhi Taufik Hidayat Taufiq Hidayat Tawakkal, M. Iqbal Wulan, Diah Nawang Wulandari, Devi Ayu Zakki Alawi Zeti Novitasari