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All Journal Telematika Sistemasi: Jurnal Sistem Informasi Jurnal Informatika Indonesian Journal on Software Engineering (IJSE) Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Paradigma Jurnal Komputasi Techno Nusa Mandiri : Journal of Computing and Information Technology JURNAL TEKNOLOGI DAN OPEN SOURCE INTI Nusa Mandiri JATI (Jurnal Mahasiswa Teknik Informatika) Infotech: Jurnal Informatika & Teknologi Journal of Informatics Management and Information Technology Jurnal PkM (Pengabdian kepada Masyarakat) Reputasi: Jurnal Rekayasa Perangkat Lunak SPEED - Sentra Penelitian Engineering dan Edukasi JUSTIN (Jurnal Sistem dan Teknologi Informasi) Journal of Innovative and Creativity Switch: Jurnal Sains dan Teknologi Informasi
Siti Khotimatul Wildah
Universitas Bina Sarana Informatika
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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ANALISIS KINERJA ALGORITMA ENSEMBLE DALAM PREDIKSI PERILAKU PEMBELIAN PELANGGAN Latif, Abdul; Khotimatul Wildah, Siti
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 1 (2025): JATI Vol. 9 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i1.12413

Abstract

Prediksi perilaku pembelian pelanggan merupakan salah satu aspek terpenting dalam menentukan efektivitas strategi pemasaran sebuah perusahaan. Dalam dunia bisnis, kemampuan untuk memprediksi pola pembelian pelanggan sangat memengaruhi keputusan strategis, seperti pengelolaan inventaris dan personalisasi penawaran. Selain melakukan prediksi perilaku pembelian, pemilihan model atau algoritma yang digunakan juga menjadi faktor krusial dalam memastikan hasil prediksi yang akurat. Penelitian ini menguji kinerja empat algoritma ensemble, yaitu Gradient Boosting, AdaBoost, XGBoost, dan LightGBM, menggunakan dataset perilaku pembelian pelanggan yang mencakup berbagai atribut relevan. Hasil analisis menunjukkan bahwa algoritma ensemble memberikan performa yang sangat baik dalam memprediksi perilaku pembelian pelanggan. Gradient Boosting memberikan nilai akurasi tertinggi sebesar 92,94% dengan tingkat konsistensi yang baik dengan nilai standar deviasi sebesar 0, 0119, diikuti oleh XGBoost dengan akurasi 92,87% dan standar deviasi sebesar 0,0281, AdaBoost sebesar 92,53% dan standar deviasi sebesar 0, 0137serta LightGBM mencapai akurasi sebesar 92,00% dan standar deviasi sebesar 0, 0296. Gradient Boosting terbukti unggul dalam menghasilkan prediksi yang akurat dan konsisten, sementara XGBoost dan LightGBM menawarkan kecepatan dan efisiensi yang tinggi.
Optimisasi Model Deep Learning untuk Deteksi Penyakit Daun Tebu dengan Fine-Tuning MobileNetV2 Aryanti, Riska; Agustiani, Sarifah; Wildah, Siti Khotimatul; Arifin, Yosep Tajul; Marlina, Siti; Misriati, Titik
Journal of Informatics Management and Information Technology Vol. 4 No. 4 (2024): October 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v4i4.411

Abstract

Sugarcane leaf diseases are a serious threat in sugarcane farming because they can significantly reduce productivity and can cause major losses in yields if not detected early. Therefore, fast and accurate disease management is needed to prevent further losses. This study aims to develop a deep learning model based on MobileNetV2 with fine-tuning techniques to effectively detect sugarcane leaf diseases. Fine-tuning is a method used to adjust the parameters of a pre-trained model on a more specific target dataset. The dataset contains images of sugarcane leaves that have been classified per class based on the type of disease. In this study, fine-tuning was performed on the MobileNetV2 architecture that had been previously trained using the sugarcane leaf dataset. The fine-tuning process was carried out by rearranging the top few layers of MobileNetV2 and adding a special classification layer to predict the class of sugarcane leaf diseases. The model was trained through two stages: initial training to obtain a baseline performance and fine-tuning by opening several layers of MobileNetV2. In the initial evaluation, the model achieved a validation accuracy of 93.12%. After fine-tuning, the accuracy increased to 95.01%, indicating that this technique was able to significantly improve disease detection capabilities. The results of this study provide important contributions in the field of agriculture, especially in supporting the sustainability of sugarcane production through artificial intelligence-based technology. The implementation of the proposed model is expected to help farmers detect diseases more quickly and take timely preventive measures, thereby reducing losses.
Peningkatan Literasi Digital Anak Melalui Penyuluhan Dunia Digital yang Aman dan Bijak di RT.002 RW.009, Kelurahan Kwitang Ade Suryadi; Ricki Sastra; Suharyanto Suharyanto; Siti Khotimatul Wildah; Siti Laila Wahyuni; Khaila Anjani; Siti Nurjanah; Muhamad Iqbal Irsyad
Switch : Jurnal Sains dan Teknologi Informasi Vol. 3 No. 4 (2025): Juli: Switch : Jurnal Sains dan Teknologi Informasi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/switch.v3i4.436

Abstract

This community service program aims to improve children's digital literacy in RT.002 RW.009, Kwitang Subdistrict through structured educational outreach. The method applied is a participatory action research approach, involving pre- and post-test assessments using a digital literacy questionnaire. The results showed a significant increase in the average literacy score from 55.3 (pre-test) to 82.6 (post-test), confirming the effectiveness of the intervention. Key improvements were observed in awareness of online safety, understanding of hoaxes, and ethical social media use. This initiative contributes to informed and secure digital behavior in urban communities.
Chicken Disease Detection Based on Fases Image Using EfficientNetV2L Model Mustopa, Ali; Sasongko, Agung; Nawawi, Hendri Mahmud; Wildah, Siti Khotimatul; Agustiani, Sarifah
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.2807

Abstract

Livestock farming requires technological innovation to increase productivity and efficiency. Chickens are a livestock animal with good market prospects. However, not all farmers understand about chicken diseases and signs of sickness. Detection of chicken diseases can be done through various methods, one of which is by looking at the shape of the chicken's feces. Images in feces can be detected using machine learning. Convolutional Neural Networks (CNN) are used to speed up disease prediction. Transfer learning is used to leverage knowledge that has been learned by previous models. In this study, we propose our own CNN architecture model and present research by building a new model to detect and classify diseases in chickens through their feces. The model training process is carried out by inputting training data and validation data, the number of epochs, and the created checkpointer object. The hyperparameter tuning stage is carried out to increase the accuracy rate of the model. The research is conducted by testing datasets obtained from the Kaggle repository which has images of coccidiosis, salmonella, Newcastle, and healthy feces. The results of the study show that our proposed model only achieves an accuracy rate of 93%, while the best accuracy rate in the study is achieved by using the EfficientNerV2L model with the RMSProp optimizer, which is 97%.
Pengembangan Sistem Informasi Akademik untuk Meningkatkan Efektivitas Pengelolaan Data pada SMK Mihadunal Ula Agustiani, Sarifah; Pribadi, Denny; Dalis, Sopiyan; Wildah, Siti Khotimatul; Mustopa, Ali
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
Optimization of Random Forest Model with SMOTE for Fetal Health Classification Based on Cardiotocography Latif, Abdul; Siti Khotimatul Wildah
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4360

Abstract

Fetal health during pregnancy is a crucial aspect in ensuring the optimal growth and development of a child, particularly during the golden period of life from the womb to the age of two. In the medical field, monitoring fetal conditions is vital to detect potential risks as early as possible. One of the tools commonly used in this process is cardiotocography (CTG), which provides essential data on fetal heart activity and movement. With technological advancements, machine learning-based approaches are increasingly being utilized to process CTG data more effectively. However, a major challenge in classifying medical data such as CTG lies in class imbalance, where the distribution between majority and minority classes is uneven. This study evaluates the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) in addressing this imbalance and assesses the performance of the Random Forest algorithm in classifying fetal health conditions. The results show that the combination of SMOTE and Random Forest achieves the best performance compared to other methods, with an accuracy of 94.40%, precision of 94.45%, recall of 94.40%, and an F1-score of 94.38%. These findings indicate that SMOTE is effective in improving the representation of minority classes, while Random Forest demonstrates superior and consistent classification performance on CTG data
Website-Based Futsal Field Reservation Information System At The Cisaat Disen Gor Tya Septiani Nurfauzia Koeswara; Siti Khotimatul Wildah
Journal of Innovative and Creativity Vol. 5 No. 2 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i2.3107

Abstract

The study purpose was to develop a web-based futsal court reservation information system for GOR Disen Cisaat to address inefficiencies in the existing manual booking process, such as data loss, double bookings, and delays in generating reports. The aim was to improve customer satisfaction, streamline data management, and enhance operational efficiency for the facility’s administrators. Materials and methods involved requirements analysis, system design (including UI, database schema, and navigation structure), implementation using PHP Laravel framework with MVC architecture, and MySQL as the database. The development process included building modules for user registration, booking management, payment tracking, and administrative reporting. The system was tested through functional testing scenarios to validate features and ensure reliability before deployment. Results showed that the implemented system successfully enabled customers to make online reservations anytime and anywhere, while administrators could manage court availability, bookings, and payments more efficiently. The system eliminated double bookings, accelerated report generation, and provided a structured, centralized database for reservation data. Conclusions indicate that the web-based reservation system significantly improves the management process at GOR Disen Cisaat, offering convenience for both users and administrators. Future enhancements are recommended, including stronger data security measures, mobile responsiveness, integration with online payment gateways, automated notifications, and periodic system evaluations to maintain service quality.
Co-Authors Abdul Latif Abdul Latif Abdul Latif Ade Suryadi Agung Sasongko Agus Junaidi Agustiani, Sarifah Ahmad Al Kaafi Ai Ilah Warnilah Ali Mustopa, Ali Andi Saryoko Ari Abdilah Arifin, Yosep Tajul Ganda Wijaya Gata, Windu Hendri Mahmud Nawawi Ikrom Mauludin Salam Khaila Anjani Laksono Trisnantoro M. Rangga Ramadhan Saelan Maysaroh, Maysaroh Muhamad Iqbal Irsyad Muhammad Sony Maulana Mustopa, Ali Nanik Wuryani Nawawi, Hendri Mahmud Pribadi, Denny Rahmat Hidayat Ricki Sastra Riska Aryanti Rizky Ade Safitri Rusda Wajhillah, Rusda Sandra Jamu Kuryanti - Universitas BSI Sanggayasa Sanggayasa Siti Laila Wahyuni Siti Marlina, Siti SITI NURJANAH Sopiyan Dalis Suharyanto Suharyanto Suharyanto SUHARYANTO SUHARYANTO Suparni Suparni Titik Misriati Toto Haryanto Tya Septiani Nurfauzia Koeswara Windu Gata Windu Gata Yoseph Tajul Arifin Yudhistira Yudhistira