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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Jurnal Simetris TEKMAPRO Journal of Industrial Engineering and Management Prosiding SNATIF Jurnal Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Mantik Penusa SISFO Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Ekonomi dan Bisnis Jurnal SITECH : Sistem Informasi dan Teknologi Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Aisyah Journal of Informatics and Electrical Engineering bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknologi Informatika dan Komputer Indonesian Journal of Technology, Informatics and Science (IJTIS) Journal of Applied Data Sciences Jurnal Dialektika Informatika (Detika) Journal La Multiapp Bulletin of Computer Science Research Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Blockchain, Nfts and Metaverse Technology Journal of Primary Education INOVTEK Polbeng - Seri Informatika Jurnal Bina Informatika dan Komputer (BINER)
Ahmad Abdul Chamid
Universitas Muria Kudus
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Application of Naïve Bayes for Sentiment Analysis of Shopee App User Comments Muhammad Dwiky Candra Fardani; Esti Wijayanti; Ahmad Abdul Chamid
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2854

Abstract

The growth of e-commerce has transformed consumer behavior, with Shopee emerging as one of the leading platforms in Southeast Asia and particularly dominant in Indonesia. Millions of user reviews on the Google Play Store capture diverse experiences, yet their unstructured nature hinders efficient extraction of actionable insights. This study addresses the challenge by developing an automated sentiment analysis system for Shopee user reviews, focusing on the effective use of the Naïve Bayes algorithm for Indonesian-language data. While Naïve Bayes is widely applied in text classification, this research distinguishes itself by integrating rigorous preprocessing tailored to colloquial and context-specific Indonesian app reviews, coupled with TF-IDF weighting, to enhance classification performance. A dataset of 4,000 reviews was collected via web scraping, labeled automatically based on user ratings, and split into 80% training and 20% testing subsets. Preprocessing included cleaning, case folding, tokenization, and stemming to standardize textual input. The proposed model achieved an accuracy of 83%, precision of 81%, recall of 90%, and F1-score of 85%, indicating strong performance despite class imbalance and the prevalence of ambiguous or sarcastic expressions. The results demonstrate that a lightweight probabilistic classifier, when combined with domain-specific preprocessing, can yield competitive accuracy while maintaining computational efficiency. This study contributes to sentiment analysis research in underrepresented linguistic contexts and offers a practical framework for e-commerce platforms to systematically interpret large-scale user feedback, prioritize feature improvements, and enhance customer satisfaction strategies.
TOPIC MODELING OF PUBLIC DISCOURSE ON TWITTER ABOUT THE ASSET CONFISCATION BILL USING LATENT DIRICHLET ALLOCATION (LDA) Azka Bima Aditya; Ahmad Abdul Chamid; Rizkysari Mei Maharani
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i2.477

Abstract

This study examines the structure of public discourse on Twitter regarding the Indonesian Asset Confiscation Bill, a policy initiative aimed at strengthening anti corruption enforcement and ensuring legal certainty. Moving beyond conventional sentiment classification, this research identifies how substantive public concerns are thematically organized within digital debate. A total of 14,319 cleaned and deduplicated tweets collected between January and September 2025 were analyzed using Latent Dirichlet Allocation with the optimal model configuration of nine topics selected based on coherence evaluation to ensure semantic interpretability. The findings reveal nine dominant thematic clusters, with law enforcement and regulatory enactment emerging as the primary focus, followed by legislative process dynamics, protest mobilization, party politics, and institutional accountability. These results indicate that online discourse is structured around normative concerns, particularly procedural clarity, fairness, and institutional legitimacy, rather than driven solely by emotional polarity. Scientifically, this study contributes by shifting the analytical emphasis from sentiment polarity toward systematic thematic mapping of digital political discourse using an optimized LDA framework tailored to Indonesian Twitter data characteristics. Practically, the findings provide policymakers with an evidence based monitoring instrument to identify priority public concerns, strengthen legislative communication strategies, and reduce interpretive ambiguity in sensitive regulatory deliberations.
Penerapan Algoritma Support Vector Machine Dalam Analisis Sentimen Terhadap Ulasan Pengguna Pada Aplikasi NewSakpole Renaldi Irfan Firdaus; Ahmad Abdul Chamid; Ahmad Jazuli
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 2 (2026): April 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i2.3478

Abstract

To determine the success rate of a service, it is necessary to conduct sentiment analysis to understand public opinion and the level of public satisfaction, both positive, neutral, and negative. Sentiment analysis was used to improve the quality of service on the NewSakpole application in android-based vehicle tax payments. In this study, several stages were used such as data crawling, data preprocessing, word weighting using TF-IDF (Term Frequency Inverse Document Frequency) and SVM (Support Vectore Machine) classification model for sentiment classification. By using the Confusionsion Matrix test with a data sharing of 20% for data training and 80% for data testing, the accuracy results were obtained of 81.08%. The accuracy value of each sentiment also showed a fairly good performance, with a positive sentiment of 81.11%, a neutral sentiment of 78.62%, and a negative sentiment of 83.33%. These results show that the SVM algorithm is able to provide a fairly stable classification performance in this case study.Keywords: Sentimen Analysis; Google Play Store; NewSakpole; Support Vectore Machine AbstrakUntuk mengetahui tingkat keberhasilan suatu pelayanan, perlu dilakukan analisis sentimen untuk memahami opini publik dan tingkat kepuasan publik baik positif, netral, maupun negatif. Analisis sentimen digunakan untuk meningkatkan kualitas pelayanan pada aplikasi NewSakpole dalam pembayaran pajak kendaraan yang berbasis android. Dalam penelitian ini menggunakan beberapa tahap seperti crawling data, preprocessing data, pembobotan kata menggunakan TF-IDF (Term Frequency Inverse Document Frequency) serta model klasifikasi SVM (Support Vectore Machine) untuk klasifikasi sentimen. Dengan menggnakan pengujian Confussion Matrix dengan pembagian data 20% untuk data training dan 80% untuk data testing memperoleh hasil akurasi sebesar 81,08%. Nilai akurasi pada masing-masing sentimen juga menunjukkan performa yang cukup baik, dengan sentimen positif sebesar 81,11%, sentimen netral 78,62%, dan sentimen negatif 83,33%. Hasil ini menunjukkan bahwa algoritma SVM mampu memberikan performa klasifikasi yang cukup stabil pada studi kasus ini. 
Enhancing SMOTE-ENN Efficacy on Imbalanced Datasets Using Decision Tree Leaf Feature Extraction: A Case Study on Student Employability Data Rizkysari Meimaharani; Widowati Widowati; Ahmad Abdul Chamid
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1396

Abstract

This study looks at the challenge of classifying tabular data that is highly imbalanced and overlapping, where standard predictive models often lose performance and tend to focus too much on the majority class. Another problem is that many advanced ensemble models are highly complex and lack transparency. These models are often viewed as black boxes, making it difficult for users to clearly and explain how each feature contributes to the final prediction result.This study offers a hybrid classification approach to address the problem, by combining rule extraction from decision tree leaves, SMOTE-ENN resampling technique, and XGBoost algorithm to improve prediction performance more accurately and reliably.The leaf extraction process helps reorganize the data by separating overlapping class regions into clearer and more structured groups before synthetic samples are generated. The test results show that the proposed approach is able to exceed the performance of the baseline model, by obtaining an F1-score of 0.8554 which indicates increased effectiveness and balance in prediction. In addition to improving performance, this method also keeps the model interpretable. Instead of relying only on abstract engineered features, the model allows us to trace important features back to the original decision tree rules. This approach helps explain the prediction formation process more transparently, so that each model decision can be understood clearly, logically, and easily interpreted. Overall, the combination of Decision Tree, SMOTE-ENN, and XGBoost is effective in handling extreme class imbalance, while producing a clear, stable, and easy-to-understand model, making it more reliable and trustworthy in various real-world applications.
CLIENT-SIDE ONLINE GAMBLING DETECTION USING MULTI-LAYER CASCADE PATTERN MATCHING IN MANIFEST V3 CHROME EXTENSIONS Wingga Aria Sasra; Ahmad Abdul Chamid; Ahmad Jazuli
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.549

Abstract

Online gambling sites in Indonesia generated IDR 155.4 trillion in transactions in 2025 with 3.2 million active players, yet DNS filtering the dominant countermeasure blocks only 0.64% of observed gambling traffic. Network-layer approaches fail structurally: they cannot intercept content via VPN, DNS-over-HTTPS, or direct IP access, and they cannot detect the domain neutralization used by the majority of Indonesian gambling operators. This paper proposes GUPI (Gambling URL Pattern Interceptor), a Chrome Extension implementing a three-layer cascade detection architecture running entirely client-side under Manifest V3 without external server dependencies. Layer 1 applies weighted lexical scoring to URL features. Layer 2 applies DOM keyword pattern matching with conditional context suppression. Layer 3 applies CSS selector-based DOM structural heuristic scoring to detect gambling-characteristic page architectures when text-level signals are absent. GUPI was evaluated on 926 URLs (326 gambling, 600 benign) across three sequential configurations. The full system achieves 98.81% accuracy, 99.07% precision, 97.55% recall, 98.30% F1-score, and 0.50% false positive rate. 
Web-Based Customer Loyalty Point System Using QR Code with Whatsapp Notification and Reward Management at Bismole Elektrik Store Qatrhunnada Abiyu Akhdan; Aditya Akbar Riadi; Ahmad Abdul Chamid
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 3 (2026): August
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/apv6sm19

Abstract

The development of information technology encourages retail business operators to implement digital systems to improve service quality and operational efficiency. At Bismole Elektrik Store, the processes of recording customers, purchase transactions, and calculating loyalty points were previously done manually, leading to data-recording errors and slowing down the service process. Based on the observation of 120 customer transaction data and interviews with 2 store owners and cashiers, several issues were found, such as difficulties in searching for customer data and discrepancies in point calculations. This research aims to develop a web-based customer loyalty point system using QR codes as a digital customer identity integrated with reward management, sales reports, and real-time WhatsApp notifications. The system development uses the Waterfall method, which consists of the stages of requirements analysis, design, implementation, testing, and maintenance. The system is developed using the programming languages PHP, HTML, CSS, JavaScript, and the MySQL database. The system evaluation was conducted using the black-box testing method with 9 testing scenarios and user acceptance testing involving 5 users consisting of the store owner, cashier, and customers. The results of the black box testing showed that all system features operated with a success rate of 100%, while the user acceptance testing results indicated a user satisfaction level of 92%, demonstrating that the system is easy to use and capable of supporting store operational activities. The research results show that the implementation of QR codes can accelerate the customer identification process, automate point calculations, manage the reward redemption process, and provide transaction information through WhatsApp notifications. Thus, the developed system can enhance the efficiency and accuracy of managing the customer loyalty program at Toko Bismole Elektrik.
KLASIFIKASI RISIKO GAGAL BAYAR KREDIT MENGGUNAKAN XGBOOST DENGAN MODEL EXPLAINABLE BERBASIS SHAP Aditia Ananda Sutrisno; Ahmad Abdul Chamid; Rizkysari Meimaharani
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 1 (2026): JATI Vol. 10 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i1.16673

Abstract

Risiko gagal bayar kredit merupakan permasalahan penting dalam sektor keuangan yang berpotensi mengganggu stabilitas lembaga keuangan apabila tidak dikelola secara efektif. Permasalahan utama dalam prediksi risiko kredit moderen adalah kebutuhan akan model yang tidak hanya memiliki akurasi tinggi, tetapi juga mampu memberikan penjelasan yang transparan terhadap hasil prediksi. Penelitian ini bertujuan untuk mengembangkan model klasifikasi risiko gagal bayar kredit menggunakan algoritma Extreme Gradient Boosting (XGBoost) serta menganalisis interpretabilitas model melalui pendekatan Explainable Artificial Intelligence berbasis Shapley Additive Explanations (SHAP). Metode penelitian meliputi tahap preprocessing data berupa pembersihan data, imputasi nilai hilang menggunakan KNN Imputer, encoding variabel kategorikal, normalisasi data, pelatihan model XGBoost, serta evaluasi kinerja menggunakan metrik akurasi, precision, recall, dan F1-score. Dataset yang digunakan berasal dari Kaggle dengan jumlah 32.586 data nasabah dan 13 fitur. Hasil penelitian menunjukkan bahwa model XGBoost mampu mencapai akurasi sebesar 0,93, dengan nilai recall sebesar 0,98 untuk kelas no default dan 0,74 untuk kelas default. Analisis SHAP mengidentifikasi customer_income, loan_grade, loan_amnt, dan loan_int_rate sebagai fitur yang paling berpengaruh terhadap prediksi risiko gagal bayar. Hasil ini menunjukkan bahwa integrasi XGBoost dan SHAP mampu menghasilkan model prediksi risiko kredit yang akurat sekaligus transparan, sehingga dapat mendukung pengambilan keputusan dalam sistem evaluasi risiko kredit
PERBANDINGAN MODEL DECISION TREE, RANDOM FOREST, DAN SVM PADA ANALISIS SENTIMEN BERBASIS ASPEK KOMENTAR FILM JUMBO Heru Teguh Apriyanto; Ahmad Abdul Chamid; Rizkysari Meimaharani
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 1 (2026): JATI Vol. 10 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i1.16678

Abstract

Industri perfilman Indonesia, khususnya genre animasi, mengalami perkembangan signifikan dengan kehadiran film "Jumbo" (2024). YouTube sebagai platform utama diskusi film menghasilkan ribuan komentar tidak terstruktur yang menyulitkan pemahaman objektif terhadap persepsi publik pada aspek-aspek spesifik film seperti cerita, visual, musik, dukungan, dan perbandingan. Penelitian ini bertujuan mengidentifikasi aspek yang paling banyak dibahas dan membandingkan performa tiga algoritma machine learning (Decision Tree, Random Forest, dan SVM) dalam mengklasifikasikan sentimen berbasis aspek. Menggunakan Aspect-Based Sentiment Analysis (ABSA) dengan pendekatan lexicon-based untuk pelabelan sentimen. Sebanyak 7.906 komentar dikumpulkan dari lima kanal YouTube, diproses melalui preprocessing, identifikasi aspek berdasarkan kata kunci, pelabelan sentimen, dan ekstraksi fitur TF-IDF. Tiga model klasifikasi dilatih dan dievaluasi pada 4.082 komentar berlabel. Decision Tree mencapai performa terbaik dengan rata-rata akurasi 91,0%, tertinggi pada aspek cerita_emosi (99,6%) dan terendah pada musik (78,9%). Aspek dukungan_apresiasi paling dominan (1.797 komentar, 88,7% sentimen positif), mengindikasikan respons positif publik. Penelitian ini memberikan wawasan objektif persepsi audiens dan membuktikan efektivitas ABSA untuk analisis ulasan film Indonesia
PERANCANGAN SISTEM INFORMASI E-COMMERCE DAN MANAJEMEN STOK BERBASIS WEBSITE UNTUK MENINGKATKAN EFISIENSI TRANSAKSI PADA TOKO SEMBAKO: Niki Nilam Sari; Rizkysari Mei Maharani; Ahmad Abdul Chamid
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 1 (2026): JATI Vol. 10 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i1.17103

Abstract

Perkembangan teknologi informasi mendorong pelaku usaha mikro, kecil, dan menengah (UMKM) untuk memanfaatkan sistem informasi dalam mendukung kegiatan operasional. Toko Sembako Rizky merupakan salah satu UMKM yang sebelumnya masih melakukan pencatatan stok dan transaksi penjualan secara manual, sehingga sering mengalami kendala seperti kesalahan pencatatan, keterlambatan laporan, dan kesulitan dalam memantau stok secara real-time. Oleh karena itu, penelitian ini bertujuan untuk merancang dan membangun sistem informasi e-commerce dan manajemen stok berbasis web yang dapat meningkatkan efisiensi dan akurasi pengelolaan data. Metode pengembangan sistem yang digunakan meliputi analisis kebutuhan, perancangan sistem menggunakan Unified Modeling Language (UML), implementasi sistem berbasis web, serta pengujian fungsionalitas sistem. Sistem yang dikembangkan memiliki fitur utama berupa pengelolaan data produk, pencatatan stok masuk dan keluar, transaksi penjualan online dan offline, laporan penjualan dan stok, serta manajemen pengguna berdasarkan hak akses. Hasil implementasi menunjukkan bahwa sistem mampu mempercepat proses pencatatan transaksi dan stok, meningkatkan akurasi data, serta memudahkan pemilik toko dalam memantau kondisi usaha dan mengambil keputusan. Dengan demikian, sistem informasi yang dibangun dapat menjadi solusi efektif dalam mendukung pengelolaan usaha Toko Sembako Rizky secara lebih terstruktur, efisien, dan berkelanjutan.
IMPLEMENTASI SISTEM PENGGAJIAN KARYAWAN BERBASIS WEB UNTUK EFISIENSI OPERASIONAL TOKO INAYA KOSMETIK Richa Yolanda; Rizkysari Mei Maharani; Ahmad Abdul Chamid
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 1 (2026): JATI Vol. 10 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i1.17166

Abstract

Perkembangan teknologi informasi mendorong pelaku usaha untuk memanfaatkan sistem informasi dalam meningkatkan efisiensi dan akurasi proses administrasi, termasuk pada pengelolaan penggajian karyawan. Toko Inaya Kosmetik masih menerapkan sistem penggajian dan pencatatan kehadiran secara manual, sehingga menimbulkan permasalahan berupa lamanya proses perhitungan gaji, kesulitan penelusuran data kehadiran, serta keterbatasan akses informasi penggajian bagi karyawan. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem informasi penggajian karyawan berbasis web guna meningkatkan efisiensi, akurasi, dan transparansi proses administrasi penggajian. Metode penelitian yang digunakan adalah pengembangan sistem dengan model Waterfall. Sistem dibangun menggunakan bahasa pemrograman PHP dan basis data MySQL. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu mempercepat proses penggajian, meminimalkan kesalahan perhitungan, serta mempermudah pengelolaan data karyawan dan akses informasi gaji secara mandiri. Dengan demikian, sistem informasi penggajian berbasis web ini dapat menjadi solusi yang efektif dalam mendukung efisiensi operasional dan pengelolaan sumber daya manusia di Toko Inaya Kosmetik
Co-Authors Abraham Wahyu Wicaksono Aditia Ananda Sutrisno Aditya Akbar Riadi Ahdi Riyono Ahmad Hariyadi, Ahmad Ahmad Jazuli Ahmad Jazuli Akbar, Fadhil Akh Sokhibi Akhmad Akhmad Alif Catur Murti Alif Catur Murti Alvin Rainaldy Hakim Anteng Widodo Aprilianto, Mohammad Andrean Jaya ardiansyah, bangga aditya Arfiyan khusnul Umam Arfiyan Khusnul Umam Arya Yudha Ananta Wijaya Azka Bima Aditya Bayu Surarso Danang Budiman Hidayat Dimas Satrio Oktavianto Endang Supriyati Enno Siti Nurainin Esti Wijayanti Esti Wijayanti, Esti Evita Noor Sofiana Dewi Fahri Muhammad Daelami fatmarini, dini Fika Kamalul Wafi Hendra Dwi Kurniawan, Hendra Dwi Hendrawan, Andra Putra Heru Teguh Apriyanto Ida Nur Aeni Ida Nur Aeni, Ida Nur Intan Nabila Hilma Iqbal Haqiqi Ariyanto Ivan Bagus Prasetiyo Juwanda, Farikhin Mahendra, Bagus Rosa marta, amelia vidora revita Maula Yudhananta, Naufal Maulana, Sahidin Achmad Noor Mohammad Kanzunnudin Muhammad Dwiky Candra Fardani Muhammad Imam Ghozali Muhammad Rieza Maulana Muhammad Rizki Naila Rizki Salisa Niki Nilam Sari Noor Azizah Prayogo, Sandi Qatrhunnada Abiyu Akhdan Ratih Nindyasari Renaldi Irfan Firdaus Richa Yolanda Rijal, Syaifur Rina Fiati Rizky Sari Mei Maharani Rizkysari Mei Maharani Rizkysari Mei Maharani Rizkysari Meimaharani Rizkysari Meimaharani Safitri, Sabrina Alya Salsabilah, Nisrina Rona Sentanu, Arga Seva Amartya Shafian Syah, Aliffaza Sholihul Ibad Sokhibi, Akh Sri Utaminingsih Syafri Samsudin Teguh, Aris Wicakosno Wibowo Harry Sugiharto Wibowo, Yuniar Satrio Widowati Widowati Wingga Aria Sasra