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All Journal Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) INTI Nusa Mandiri Amalee: Indonesian Journal of Community Research & Engagement Jurnal Sistem Komputer dan Informatika (JSON) International Journal of Engineering, Science and Information Technology Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Decode: Jurnal Pendidikan Teknologi Informasi Indonesian Community Journal Jurnal Pengabdian, Riset, Kreativitas, Inovasi, dan Teknologi Tepat Guna (Parikesit) Jurnal Teras Pengabdian Masyarakat Scientific: Journal of Computer Science and Informatics Indonesian Applied Research Computing and Informatics
Afril Efan Pajri
Universitas Nahdatul Ulama Sunan Giri
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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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.
Digitalisasi untuk Penguatan Tata Kelola Koperasi Desa Merah Putih Afril Efan Pajri; Ita Aristia Sa’ida
Jurnal Teras Pengabdian Masyarakat Vol. 2 No. 1: Januari (2026)
Publisher : PT. Teras Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64479/jtpm.v2i1.54

Abstract

Digital transformation in cooperatives has become an urgent necessity in the era of Industry 4.0 and Society 5.0. This study aims to develop and implement a web-based cooperative management system in Koperasi Desa Merah Putih to enhance efficiency, accuracy, transparency, and member participation. The research employed a research and development (R&D) method with a participatory approach, involving cooperative managers and members throughout the stages of needs analysis, system design, implementation, and evaluation. The results indicate that the digital system significantly improves administrative efficiency by more than 50%, reduces recording errors by over 70%, and provides real-time financial reports accessible to members. Furthermore, the system fosters digital literacy among cooperative managers and members, although several challenges remain, such as limited internet infrastructure, human resource readiness, and the need for continuous system maintenance. Practically, this study offers a ready-to-use digital cooperative system that can serve as a model for other rural cooperatives. Academically, it strengthens the literature on cooperative digitalization as a strategy to enhance governance, build member trust, and promote sustainable rural economic development.
Sosialisasi Pemanfaatan Media Digital Dalam Penyampaian Informasi Publik Di Desa Jipo Kecamatan Kepohbaru Afril Efan Pajri; M. Iqbal Tawakkal
Jurnal Teras Pengabdian Masyarakat Vol. 1 No. 2: Juli (2025)
Publisher : PT. Teras Digital Nusantara

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

Abstract

Penggunaan media digital telah menjadi salah satu kebutuhan utama dalam penyampaian informasi publik di era modern ini. Desa Jipo, Kecamatan Kepohbaru, memiliki potensi besar untuk memanfaatkan media digital guna meningkatkan keterbukaan informasi dan partisipasi masyarakat. Penelitian ini bertujuan untuk mengidentifikasi upaya sosialisasi yang telah dilakukan serta dampak pemanfaatan media digital dalam penyampaian informasi publik. Metode yang digunakan adalah deskriptif kualitatif dengan teknik pengumpulan data melalui wawancara, observasi, dan dokumentasi. Hasil penelitian menunjukkan bahwa sosialisasi penggunaan media digital, seperti media sosial dan aplikasi pesan instan, telah memberikan pengaruh positif terhadap efisiensi komunikasi antara pemerintah desa dan masyarakat. Namun, tantangan seperti keterbatasan infrastruktur dan literasi digital masih perlu diatasi.
Perbandingan SVM dan Logistic Regression Berbasis SMOTE pada Analisis Sentimen Menteri Keuangan di YouTube Sirozul Huda; Afril Efan Pajri; Sahri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 4 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i4.9632

Abstract

YouTube telah berkembang menjadi salah satu ruang utama bagi masyarakat untuk menyampaikan opini terhadap isu publik, termasuk respons terhadap figur pejabat pemerintah. Penelitian ini bertujuan untuk mengevaluasi dan memetakan persepsi publik terhadap Menteri Keuangan Republik Indonesia melalui analisis sentimen berbasis machine learning, dengan mengkomparasikan kinerja algoritma Support Vector Machine dan Logistic Regression. Data berjumlah 4.003 data komentar  dikumpulkan dari komentar berbagai kanal YouTube berdasarkan pencarian topik terkait, kemudian melalui tahapan pre-processing dan pelabelan otomatis menggunakan Indonesian Sentiment Lexicon (InSet). Meskipun pelabelan otomatis berbasis leksikon ini efisien, pendekatan ini memiliki keterbatasan untuk mendeteksi sarkasme dan menangkap konteks kalimat yang kompleks. Setelah fitur diekstraksi menggunakan pembobotan TF-IDF, teknik Synthetic Minority Over-sampling Technique (SMOTE) diterapkan untuk mengatasi masalah ketidakseimbangan label pada data latih. Pengujian model membuktikan bahwa pendekatan yang diusulkan berhasil melakukan klasifikasi dengan sangat baik. Hasil evaluasi menunjukkan bahwa Support Vector Machine memberikan tingkat akurasi tertinggi sebesar 90%, sedikit mengungguli Logistic Regression yang mencatatkan akurasi sebesar 89%. Kontribusi ilmiah dalam studi ini menegaskan bahwa algoritma klasifikasi berbasis SMOTE efektif dalam menangani masalah ketimpangan data, dengan menunjukkan peningkatan performa model dibandingkan penelitian terdahulu yang serupa.
Strengthening Product Innovation, Managerial Capability, and Market Expansion to Enhance Revenue Generation of the ‘Putri’ Sapodilla Syrup Enterprise Siti Shoimah; Afril Efan Pajri; Fatichatur Rochmaniyah; Eka Fadlilatus Saadah; Muhammad Sholihin
Jurnal Pengabdian, Riset, Kreativitas, Inovasi, dan Teknologi Tepat Guna Vol 4 No 1 (2026): Mei
Publisher : Direktorat Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/parikesit.v4i1.27774

Abstract

Kegiatan pengabdian masyarakat dilaksanakan untuk memberdayakan Industri Rumah Tangga (IRT) Sirup Sawo ‘Putri’ di Desa Pangkatrejo, Kecamatan Sugio, Lamongan. Mitra menghadapi permasalahan pada tiga aspek utama: (1) kualitas produk yang belum konsisten dan tidak tahan lama akibat keterbatasan teknologi produksi, (2) lemahnya sistem manajemen usaha dan pencatatan keuangan, serta (3) pemasaran yang terbatas pada promosi konvensional. Metode pelaksanaan mencakup lima tahap: sosialisasi, pelatihan, penerapan teknologi tepat guna, pendampingan, dan evaluasi. Intervensi yang diberikan antara lain penggunaan mesin pasteurisasi, blender heavy duty, pH meter, refraktometer, serta vacuum sealer untuk meningkatkan higienitas dan daya simpan produk. Pada aspek manajemen, dilakukan pelatihan pencatatan keuangan digital, penyusunan identitas merek, dan pengarsipan usaha sederhana. Pada aspek pemasaran, diterapkan desain ulang kemasan melalui aplikasi Canva Pro, aktivasi akun media sosial (Instagram, TikTok), serta distribusi di e-commerce (Shopee, Tokopedia). Hasil kegiatan menunjukkan peningkatan signifikan: produk sirup mampu bertahan hingga 3 bulan tanpa pengawet, penjualan meningkat hingga 30% dalam 6 bulan, dan keterampilan manajerial mitra meningkat melalui pencatatan digital. Program ini berkontribusi pada pencapaian SDGs 8, 9, dan 12, serta mendukung penguatan ekonomi hijau berbasis potensi lokal
Analisis Perbandingan Algoritma SVM, Logistic Regression, Naive Bayes, dan XGBoost Untuk Deteksi Fake News Umar Farid Al Faqihi; Afril Efan Pajri; Muhammad Jauhar Vikri
JURIKOM (Jurnal Riset Komputer) 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.v13i1.9492

Abstract

The rapid growth of digital technology and internet access has completely changed how information is shared, enabling content to spread quickly across various online platforms. However, these advancements have also made it easier for misleading or entirely fabricated news to circulate, posing serious risks to social stability, political environments, and public health. This study tackles this problem by employing several machine learning-based classification methods for analyzing textual data. Four algorithms Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), and Extreme Gradient Boosting (XGBoost) were applied to detect linguistic patterns that differentiate genuine news from fake content. A major contribution of this research is the creation of a custom dataset gathered directly from Indonesian online news portals, comprising a total of 4,909 entries. The evaluation results demonstrate exceptionally high accuracy across the models: 99.69% for SVM, 99.39% for LR, 99.29% for NB, and 99.19% for XGBoost. To verify reliability, each model was further evaluated using cross-validation, yielding average accuracy scores of 99.57% (SVM), 99.52% (LR), 99.44% (NB), and 99.49% (XGBoost). These findings confirm that all four classifiers are highly effective and well-suited for identifying fake news in text-based data.
Studi Komparatif Algoritma Random Forest dan Logistic Regression dalam Analisis Sentimen Ulasan Aplikasi E-Wallet Dana Diah Fitriani; Afril Efan Pajri; Aprillia Dwi Ardianti
JURIKOM (Jurnal Riset Komputer) 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.v13i1.9517

Abstract

The increasing use of digital wallets in Indonesia has led to a growing number of user opinions expressed, including on the DANA platform in the Play Store. These reviews reflect users' experiences and satisfaction levels, necessitating sentiment analysis to comprehend public opinions about the application’s service quality. The study conducts an analytical comparison between Random Forest and Logistic Regression methods in classification sentiments for DANA application. Data was obtained through scraping techniques, resulting in 2,068 reviews after the cleaning process. The analysis stages include text preprocessing, labeling based on review scores, weighting using TF-IDF, and modeling with both algorithms. The evaluation results demonstrate that Random Forest obtains an accuracy of 86.23%, while Logistic Regression obtains an accuracy of 84.54%. Both models are capable of classifying positive sentiments well but are less optimal in detecting negative sentiments. Random Forest shows higher performance compared to Logistic Regression within the task in sentiment analysis for DANA app reviews. Thus, we can conclude that using the random forest algorithm is able to produce accurate sentiment analysis and can act as a basis for making decisions in further research
TikTok Sentiment Analysis on Koperasi Merah Putih Using SVM and ANN Meliysa Pasa Bagna Aprilia Said; Kholifatus Sholihah; Ifnu Wisma Dwi Prastya; Afril Efan Pajri
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13394

Abstract

TikTok has become a relevant social media source for observing public responses to public issues, including the Koperasi Merah Putih program. This study compares Support Vector Machine (SVM) and Artificial Neural Network (ANN) for classifying sentiment in TikTok comments. The dataset was obtained through TikTok comment scraping and consisted of 25,669 raw comments. After removing empty comments and applying preprocessing stages consisting of cleaning, case folding, normalization, tokenization, stopword removal, and stemming, 21,026 comments were used for sentiment analysis. Sentiment labels were generated automatically using a lexicon-based sentiment labeling approach and grouped into three classes: positive, negative, and neutral. TF-IDF was used for feature extraction with a maximum of 5,000 features and unigram-bigram representation. The dataset was split into training and testing sets with an 80:20 ratio, while Stratified K-Fold Cross Validation and SMOTE were applied to strengthen evaluation and address class imbalance. The results show that SVM achieved the best overall performance before SMOTE with an accuracy of 86.66% and an F1-score of 86.76%. ANN achieved an accuracy of 85.31% before SMOTE and improved slightly after SMOTE to 85.47%. These findings indicate that SVM is more stable for TF-IDF-based TikTok comment classification, while SMOTE can improve ANN performance slightly but does not always increase all models equally.
Comparison of the Performance of K-Nearest Neighbor and Naive Bayes Algorithms for Sentiment Analysis of PinjamYuk Application User Reviews Using SMOTE and TF-IDF Lindya Rossita Handoko; Amelia Faza; Mula Agung Barata; Afril Efan Pajri
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13398

Abstract

The growth of online lending services has driven the increasing adoption of digital financial applications, providing users with convenient access to financial services. This growing number of users has generated a large volume of reviews on the Google Play Store, which can serve as a valuable source for understanding users’ perspectives on the benefits and performance of these applications. This study aims to compare the performance of the K-Nearest Neighbor (KNN) and Naive Bayes algorithms in classifying the sentiment of user reviews of the PinjamYuk application. The study uses a secondary dataset obtained from Kaggle, consisting of 500 user reviews of the PinjamYuk application on the Google Play Store during the 2023–2024 period. The reviews were categorized into three sentiment classes: positive, neutral, and negative, based on their rating scores. Because the class distribution in the dataset was imbalanced, the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the classes before the classification process. The research procedure included data preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), data balancing using SMOTE, and KNN parameter optimization using GridSearchCV. The models were evaluated using accuracy, precision, recall, F1-score, confusion matrix, stratified K-fold cross-validation, and the McNemar test. The results show that the Naive Bayes algorithm outperformed KNN. The stratified K-fold cross-validation results yielded an average accuracy of 81.0% for Naive Bayes and 80.8% for KNN. Furthermore, the McNemar test produced a p-value of 0.014 (p < 0.05), indicating that the performance difference between the two algorithms was statistically significant. These findings demonstrate that the Naive Bayes algorithm is more effective for analyzing user sentiment in reviews of the PinjamYuk application.
Penguatan Kualitas Produk, Pengelolaan, dan Pemasaran Sirup Sapodilla 'Putri' Siti Shoimah; Afril Efan Pajri; Fatichatur Rochmaniyah; Eka Fadlilatus Saadah; Muhammad Sholihin
Amalee: Indonesian Journal of Community Research and Engagement Vol. 6 No. 2 (2025): Amalee: Indonesian Journal of Community Research and Engagement
Publisher : LP2M INSURI Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37680/amalee.v6i2.8124

Abstract

This community service program was carried out to empower the Home Industry (IRT) of “Putri” Sapodilla Syrup in Pangkatrejo Village, Sugio Subdistrict, Lamongan. The partner faced challenges in three main aspects: inconsistent product quality and a short shelf life due to limited production technology, weak business management systems and financial record-keeping, and a marketing strategy limited to conventional promotional methods. The implementation method consisted of five stages: socialization, training, application of appropriate technology, mentoring, and evaluation. Interventions included the use of pasteurization machines, heavy-duty blenders, pH meters, refractometers, and vacuum sealers to improve product hygiene and shelf life. In terms of management, training was provided on digital financial recording, brand identity development, and simple business archiving. In marketing, redesigned packaging was introduced through Canva Pro, the activation of social media accounts (Instagram and TikTok), and distribution via e-commerce platforms (Shopee). The results showed significant improvements: the syrup product can now last up to three months without preservatives, sales increased by up to 30% within six months, and the partner’s managerial skills improved through digital record-keeping. Specifically, the partner’s average monthly revenue increased from IDR 3,000,000 before the program to approximately IDR 4,000,000 - 4,200,000 after the interventions. This program contributes to the achievement of SDGs 8, 9, and 12, and supports the strengthening of a green economy based on local potential.  
Co-Authors Alif Yuanita Kartini Amelia Faza Aprillia Dwi Ardianti Arum , Arum Fatmawati Arum Fatmawati Arum Denny Nurdiansyah Diah Fitriani Dwi Remawati Eka Fadlilatus Saadah Eka Fadlilatus Saadah Fatichatur Rochmaniyah Fatichatur Rochmaniyah Fikram, Muhammad Ifnu Wisma Dwi Prastya Ita Aristia Sa'ida Jauhar Vikri, Muhammad Jauhara Rana Budiani, Jauhara Rana Khairunnisa Kholifatus Sholihah Kumaratih Sandradewi Lindya Rossita Handoko M. Iqbal Tawakkal MARATUS SHOLIHAH, SITI Maruf, Anang Meliysa Pasa Bagna Aprilia Said Muhammad Sholihin Muhammad Sholihin Mula Agung Barata Nanda Sahita Pepbelia Veronyca Putri Liana Sabar, Rohmat Abidin Sahri Sahri Sahri Sahri Sahri Sahri Silviana Agustin Sirozul Huda Siti Azizah Aini Siti Shoimah Sri Hariyati Fitriasih Suci Mulyani Sutriawan Taufik Azhary Tawakkal, M. Iqbal Umar Farid Al Faqihi Wella Vernanda Meilasaroh