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All Journal Telematika Sistemasi: Jurnal Sistem Informasi Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal on Software Engineering (IJSE) Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Paradigma Jurnal Komputasi JATI (Jurnal Mahasiswa Teknik Informatika) JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Teknik Informatika C.I.T. Medicom Akrab Juara : Jurnal Ilmu-ilmu Sosial Infotech: Jurnal Informatika & Teknologi Journal of Informatics Management and Information Technology Jurnal Abdimas Ekonomi dan Bisnis Reputasi: Jurnal Rekayasa Perangkat Lunak IMTechno: Journal of Industrial Management and Technology Sinergi: Jurnal Pengabdian Kepada Masyarakat Bulletin of Informatics and Data Science Journal of Artificial Intelligence and Technology Information Help: Journal of Community Service (HJCS) Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat Smatika Jurnal : STIKI Informatika Jurnal Jurnal Informatika Jurnal Komputasi
Agustiani, Sarifah
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry 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 Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Social Sciences Other

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Klasifikasi Penyakit Daun Padi menggunakan Random Forest dan Color Histogram Sarifah Agustiani; Yoseph Tajul Arifin; Agus Junaidi; Siti Khotimatul Wildah; Ali Mustopa
Jurnal Komputasi Vol. 10 No. 1 (2022)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v10i1.2961

Abstract

Indonesia is an agrarian country, which is a sector that plays an important role most of the Indonesian population makes agriculture the main focus, but the function of rice fields into housing or industry has resulted in a decrease in rice production, in addition to pests, diseases, unfavorable weather, Irrigation is not smooth resulting in less than the maximum yield. For this reason, it is necessary to have technology that can implement the process of detecting rice leaf disease in order to provide information to farmers about rice leaf damage. The most modern approach today can be done with machine learning or deep learning by using various algorithms to improve recognition and accuracy in the detection and diagnosis of plant diseases. Based on this, this study aims to propose a method of classifying rice leaf diseases in order to provide information to farmers about rice leaves which are expected to reduce the disease by detecting the disease early so as to increase rice production. In this study, the classification process is carried out using the augmented image, then the Color Histogram feature extraction method is applied, and the classification is carried out using the Random Forest algorithm. In addition, this study also conducted several comparisons, including feature extraction and yahoo to get the results, and the highest results reached 99.65% of the proposed method.
Optimasi Model Machine Learning Menggunakan Teknik SMOTE pada Analisis Sentimen Pengguna RedBus Arman Ramadhani; Riska Aryanti; Sarifah Agustiani
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 1 (2026): Volume 4 Number 1 March 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i1.182

Abstract

Perkembangan teknologi digital semakin memudahkan masyarakat dalam memenuhi kebutuhan transportasi, salah satunya melalui aplikasi pemesanan tiket bus seperti RedBus. Aplikasi ini menghadirkan layanan pemesanan secara praktis, namun ulasan pengguna yang semakin banyak di Google Play Store bersifat tidak terstruktur sehingga memerlukan analisis lebih lanjut untuk menilai kualitas layanan secara objektif. Penelitian ini bertujuan untuk mengklasifikasikan sentimen kepuasan pengguna aplikasi RedBus dengan memanfaatkan algoritma Naïve Bayes dan Random Forest. Untuk mengatasi masalah ketidakseimbangan data, digunakan teknik Synthetic Minority Over-sampling Technique (SMOTE). Data yang digunakan berjumlah 2.000 ulasan yang dikumpulkan melalui metode web scraping, kemudian diproses melalui tahapan preprocessing yang meliputi data cleaning, cleansing, case folding, tokenization, stopword, dan stemming. Selanjutnya, data diberi label kepuasan berdasarkan rating, lalu dikonversi menjadi fitur numerik dengan metode TF-IDF. Data dibagi menjadi 90% data latih dan 10% data uji agar dapat dievaluasi secara menyeluruh. Hasil pengujian menunjukkan bahwa algoritma Naïve Bayes menghasilkan akurasi 91%, precision 97%, recall 89%, dan F1-score 92%. Sementara itu, algoritma Random Forest memperoleh akurasi 90%, precision 94%, recall 90%, dan F1-score 92%. Keunggulan Naïve Bayes terlihat pada nilai precision yang tinggi, menunjukkan kemampuannya dalam meminimalkan kesalahan klasifikasi positif palsu. Kesimpulannya, penerapan Naïve Bayes dengan dukungan SMOTE dinilai lebih optimal dalam mengklasifikasikan sentimen ulasan, sehingga dapat menjadi masukan bagi pengembang RedBus dalam meningkatkan kualitas layanan dan kepuasan pengguna.
Pengembangan Sistem Informasi Akademik untuk Meningkatkan Efektivitas Pengelolaan Data pada SMK Mihadunal Ula Sarifah Agustiani; Denny Pribadi; Sopiyan Dalis; Siti Khotimatul Wildah; Ali Mustopa
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 4 No. 1 (2023): Mei 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v4i1.1992

Abstract

Teknologi informasi memiliki peran penting dalam mendukung efisiensi dan efektivitas pengelolaan data di lembaga pendidikan. SMK Mihadunal Ula, sebagai sekolah menengah kejuruan di Kabupaten Sukabumi, menghadapi tantangan dalam pengelolaan data akademik yang masih dilakukan secara manual. Hal ini menyebabkan berbagai masalah seperti kesalahan data, kesulitan akses informasi, dan keterlambatan dalam pengolahan data. Penelitian ini bertujuan untuk mengembangkan Sistem Informasi Akademik yang dapat meningkatkan efektivitas pengelolaan data pada SMK Mihadunal Ula. Metode pengembangan yang digunakan adalah pengembangan sistem Rapid Application Development (RAD) yang melibatkan proses analisis, desain, implementasi, dan evaluasi. Melalui pengembangan sistem informasi akademik, diharapkan pengelolaan data di SMK Mihadunal Ula dapat lebih terintegrasi, akurat, dan mudah diakses. Sistem ini akan menyediakan fitur-fitur penting seperti pendaftaran siswa, penjadwalan, dan pembayaran yang dapat diakses oleh siswa, guru, dan staf administrasi. Dengan adanya sistem informasi yang handal, diharapkan efisiensi operasional sekolah dapat ditingkatkan, kesalahan manusia dapat diminimalisir, dan pengambilan keputusan dapat lebih baik. Hasil penelitian ini menunjukkan bahwa implementasi Sistem Informasi Akadmik pada SMK Mihadunal Ula memberikan manfaat yang signifikan. Siswa dapat dengan mudah mendaftar, memperoleh informasi jadwal pelajaran, dan melakukan pembayaran secara efisien. Guru dan staf administrasi juga mendapatkan kemudahan dalam pengolahan data dan mengakses informasi yang diperlukan. Selain itu, penggunaan sistem informasi ini diharapkan dapat meningkatkan citra dan reputasi SMK Mihadunal Ula sebagai lembaga pendidikan yang modern dan berkualitas
Empowering Community Digital Literacy through Participatory Artificial Intelligence Training Using Participatory Action Research in South Jakarta Sarifah Agustiani; Riska Aryanti; Tri Wahyuni; Elah Nurlelah; Pristya Haliza Ramadhanti; Farah Diba Azkia
Help: Journal of Community Service Vol. 3 No. 1 (2026): June 2026
Publisher : PT Agung Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62569/hjcs.v3i1.279

Abstract

Digital literacy has become a fundamental competency for communities in responding to the rapid advancement of Artificial Intelligence (AI) technologies. However, many community members still have limited knowledge and practical skills in utilizing AI productively, ethically, and responsibly. This community service program aimed to empower community digital literacy through participatory Artificial Intelligence training using a Participatory Action Research (PAR) approach in RT 05, Cikoko Urban Village, South Jakarta. The program was implemented through four stages of PAR, including problem identification, collaborative planning, participatory action, and reflection. Training activities consisted of interactive lectures, live demonstrations, guided hands-on practice, group discussions, and mentoring on AI applications, digital ethics, information verification, and online security. Program evaluation was conducted using observations, reflective discussions, and post-training questionnaires involving sixteen participants. The findings revealed three major outcomes. First, the participatory learning approach successfully increased community engagement, with 75% female participants and 69% of participants aged 12–20 years actively involved throughout the learning process. Second, the training achieved high participant satisfaction, with information delivery, training materials, presenter performance, and event organization each receiving an 81% satisfaction score. Third, the program significantly improved community digital literacy and readiness for AI adoption. Participants reported that the program provided substantial benefits (88%), increased their knowledge (81%), improved practical AI utilization skills (81%), and enhanced their overall satisfaction (81%), while sustainable technology utilization, practical relevance, systematic implementation, and willingness to participate in future activities each achieved 75% positive responses. 
ANALISIS SENTIMEN ULASAN APLIKASI PORTAL PULSA PADA GOOGLE PLAY STORE MENGGUNAKAN METODE MACHINE LEARNING Zalukhu, Sampril Yanus; Bismi, Waeisul; Agustiani, Sarifah
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 10 No. 2 (2026): Artificial Intelligence (AI)
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v10i2.1296

Abstract

Portal Pulsa application is a pulse refilling and bill payment service platform that has a high number of reviews on the Google Play Store. These reviews can be used to determine user perception of application services. This study aims to perform sentiment analysis on Portal Pulsa application user reviews using machine learning methods. The research stages include collecting review data from the Google Play Store, text preprocessing, feature extraction using TF-IDF, and sentiment classification using several machine learning algorithms, namely Naïve Bayes, Linear Support Vector Machine (Linear SVM), Random Forest, K-Nearest Neighbor (KNN), and Decision Tree. Model evaluation was performed using accuracy, precision, recall, and F1-score metrics. The results showed that the Linear SVM algorithm provided the best performance with an accuracy value of 93.24%. These results indicate that Linear SVM is effectively used in classifying the sentiment of Portal Pulsa application user reviews.
Comparative Analysis of Transfer Learning-Based Deep Learning Models for Jatropha Leaf Disease Classification Sarifah Agustiani; Sulistiyah; Agus Junaidi; Cucu Ika Agustyaningrum; Yoseph Tajul Arifin
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i02.2325

Abstract

Plant disease identification is essential for enhancing agricultural productivity and promoting sustainable crop management practices. Jatropha curcas has considerable potential as a biofuel-producing plant; however, its growth and productivity can be significantly affected by various leaf diseases. Conventional disease diagnosis often requires substantial time and relies heavily on expert knowledge, creating a need for automated solutions based on deep learning techniques. Although deep learning has been widely applied in plant disease recognition, comparative studies focusing on transfer learning models for Jatropha leaf disease classification remain limited, particularly for datasets characterized by distinctive visual features and relatively small sample sizes. This research conducts a comparative assessment of several deep learning architectures to determine the most effective model for classifying Jatropha leaf diseases. The evaluated architectures include MobileNetV2, EfficientNetB0, ResNet50, DenseNet121, and VGG16. All models utilized ImageNet pre-trained weights and were adapted through fine-tuning of the final classification layers to accommodate a dataset containing healthy and diseased Jatropha leaf images. Experimental findings reveal that ResNet50 achieved the highest classification accuracy of 93.81%, followed by VGG16 at 93.58% and EfficientNetB0 at 90.49%. In comparison, DenseNet121 and MobileNetV2 attained accuracies of 85.40% and 74.56%, respectively. Model effectiveness was assessed using accuracy, training duration, confusion matrix analysis, and ROC curve evaluation to examine classification capability across categories. The results demonstrate that ResNet50 offers the most balanced combination of predictive accuracy and performance stability. Overall, the study confirms that transfer learning-based deep learning models are highly effective for Jatropha leaf disease classification, with ResNet50 emerging as the most suitable architecture among those investigated. These findings may serve as a valuable reference for the development of reliable and efficient plant disease detection systems in agricultural environments.
Co-Authors Abdul Latif Agung Sasongko Agus Junaidi Agus Junaidi Ali Mustopa, Ali Andi Saryoko Andreas Roy Prasetya Arifin, Yosep Tajul Arman Ramadhani Atang Saepudin Atang Saepudin Cucu Ika Agustyaningrum Elah Nurlelah Emiliana, Meutia Raissa Fachry Abda El Rahman Farah Diba Azkia Ganda Wijaya Gata, Windu Haliza Ramadhanti, Pristya Haryani Haryani Haryani Haryani Hendri Mahmud Nawawi Kamil, Anton Abdul Basah Laksono Trisnantoro M. Rangga Ramadhan Saelan Maysaroh, Maysaroh Meutia Raissa Emiliana Mustopa, Ali Nanik Wuryani Nawawi, Hendri Mahmud Prawikas, Amanda Pribadi, Denny Pristya Haliza Ramadhanti Pristya Haliza Ramadhanti Putri, Rizky Rachma Rahmat Hidayat Rahmat Hidayat Rianto, Yan Riska Aryanti Rizky Ade Safitri Rizky Rachma Putri Roy Prasetya, Andreas Rusda Wajhillah, Rusda Siti Khotimatul Wildah Siti Marlina, Siti Sopiyan Dalis Sopiyan Dalis Suharyanto Sulistiyah Syamsul Bahri Titik Misriati Tri Wahyuni Tri Wahyuni tri wahyuni Tya Septiani Nurfauzia Koeswara Waeisul Bismi Wahyudin Wahyudin Windu Gata Windu Gata Yoseph Tajul Arifin Yudhistira Yudhistira yunita yunita Zalukhu, Sampril Yanus