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All Journal International Journal of Evaluation and Research in Education (IJERE) snimed Jurnal Teknoin SMARTICS Journal Jurnal Nasional Komputasi dan Teknologi Informasi JMIE (Journal of Madrasah Ibtidaiyah Education) Jurnal Ilmiah Sinus Indonesian Journal of Business Intelligence (IJUBI) IJIIS: International Journal of Informatics and Information Systems Jurnal Teknik Industri Terintegrasi (JUTIN) International Journal of Computer and Information System (IJCIS) Jurnal Informatika Teknologi dan Sains (Jinteks) Khazanah: Jurnal Mahasiswa Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Bangun Desa : Jurnal Pengabdian Masyarakat
Dhina Puspasari Wijaya, Dhina Puspasari
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Implementasi Logika Fuzzy Tsukamoto Terhadap Pengambilan KRS Mahasiswa Informatika Universitas Alma Ata Wijaya, Dhina Puspasari; Qomaruzaman, Salis Nizar; Pramuntadi, Andri; Danianti, Dita
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 6 (2024): Desember 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i6.8277

Abstract

Abstrak - Proses pengisian Kartu Rencana Studi (KRS) merupakan langkah krusial dalam perjalanan akademik mahasiswa di perguruan tinggi, yang tidak hanya mencakup pemilihan mata kuliah tetapi juga penjadwalan dan pengaturan kelas. Dalam konteks ini, Universitas Alma Ata berupaya meningkatkan efisiensi dan efektivitas pengisian KRS melalui implementasi sistem berbasis Fuzzy Tsukamoto. Penelitian ini bertujuan untuk mengembangkan sistem rekomendasi KRS yang dapat memberikan saran berdasarkan variabel seperti Indeks Prestasi Kumulatif (IPK), mata kuliah yang diulang, dan peminatan. Dengan sistem ini, mahasiswa diharapkan dapat merancang rencana studi yang lebih optimal, mengurangi beban kerja dosen pembimbing akademik (DPA), serta meminimalkan kesalahan dalam pengisian KRS yang sering terjadi pada sistem manual saat ini. Selain itu, sistem ini dirancang untuk mengatasi tantangan seperti keterlambatan pengisian KRS, kelalaian mahasiswa dalam mengetahui mata kuliah yang mengulang, serta memberikan fleksibilitas dalam aksesibilitas. Penelitian ini juga mengidentifikasi pentingnya konsultasi dengan DPA dalam proses perencanaan studi, serta menekankan perlunya sistem yang dapat beradaptasi dengan berbagai skenario akademis, termasuk program Merdeka Belajar Kampus Merdeka (MBKM) untuk kedepannya. Dengan demikian, implementasi logika Fuzzy Tsukamoto diharapkan dapat meningkatkan akurasi, efisiensi, dan personalisasi dalam pengisian KRS, serta mendukung mahasiswa dalam mencapai tujuan akademik dan profesional mereka secara tepat waktu..Kata kunci : Kartu Rencana Studi, Logika Fuzzy Tsukamoto, Sistem Rekomendasi Berbasis Website, Perencanaan Studi, Akademik Mahasiswa Abstract - The process of filling out the Study Plan Card (KRS) is a crucial step in the academic journey of students in higher education, which includes not only course selection but also scheduling and class arrangements. In this context, Alma Ata University seeks to improve the efficiency and effectiveness of filling KRS through the implementation of a Fuzzy Tsukamoto-based system. This research aims to develop a KRS recommendation system that can provide suggestions based on variables such as Cumulative Grade Point Average (GPA), repeated courses, and specializations. With this system, students are expected to be able to design a more optimal study plan, reduce the workload of academic supervisors (DPA), and minimize errors in filling out KRS that often occur in the current manual system. In addition, this system is designed to overcome challenges such as delays in filling out KRS, student negligence in knowing which courses are repeated, and providing flexibility in accessibility. This study also identifies the importance of consultation with DPA in the study planning process, and emphasizes the need for a system that can adapt to various academic scenarios, including the Independent Learning Independent Campus (MBKM) program for the future. Thus, the implementation of Fuzzy Tsukamoto's logic is expected to improve accuracy, efficiency, and personalization in filling out KRS, as well as support students in achieving their academic and professional goals in a timely manner. Keywords - Study Plan Card, Fuzzy Tsukamoto Logic, Website-Based Recommendation System, Study Planning, Student Academic
RANCANG BANGUN SISTEM PEMESANAN JASA FOTOGRAFI DAN VIDEOGRAFI BERBASIS WEBSITE Riskiah, Putri Maulidatur; Wijaya, Dhina Puspasari; Gutama, Deden Hardan; Pramuntadi, Andri
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 2 (2025): EDISI 24
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i2.5320

Abstract

Clicks Studio, penyedia layanan fotografi dan videografi di Malang, menghadapi kendala dalam proses pemesanan yang masih dilakukan secara manual, pencatatan data yang belum optimal, serta kurangnya sistem informasi yang terstruktur untuk mengelola pelanggan. Penelitian ini bertujuan untuk merancang dan membangun sistem pemesanan jasa fotografi dan videografi berbasis website guna meningkatkan efisiensi dalam proses pemesanan, memberikan kemudahan kepada pelanggan dalam mengakses layanan, menyediakan fitur pembayaran yang lebih praktis serta mendukung pengelolaan data dan operasional studio secara lebih terorganisir. Metode pengembangan sistem yang digunakan adalah Waterfall, yang meliputi tahapan analisis kebutuhan, perancangan, implementasi, pengujian, dan pemeliharaan. Hasil penelitian menunjukkan bahwa sistem berbasis website ini dapat meminimalisir kesalahan pemesanan, mempercepat pengelolaan data pelanggan, serta meningkatkan kepercayaan dan kepuasan pelanggan. Implementasi sistem ini diharapkan dapat mendukung transformasi digital dalam industri fotografi dan videografi, khususnya di Kota Malang.
Sequential Modeling of News Headlines and Descriptions for Multi-Class Classification Pradana, Musthofa Galih; Saputro, Pujo Hari; Wijaya, Dhina Puspasari
International Journal of Computer and Information System (IJCIS) Vol 6, No 2 (2025): IJCIS : Vol 6 - Issue 2 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i2.229

Abstract

Automatic classification of news content plays a vital role in organizing and filtering data for various applications such as news recommendation systems and media monitoring. This study investigates the use of Recurrent Neural Networks (RNN) and sequential modeling for multi-class classification of news data. A dataset consisting of 12,000 news sentences, categorized into four distinct classes politics, economy, sports, and technology was utilized for training and evaluation. The research focuses on comparing the performance of RNN models without optimization techniques and RNNs enhanced through optimizer implementation and sequence modeling. The baseline RNN model, trained without any optimizer or sequence enhancements, achieved a classification accuracy of 89%. By incorporating optimizer functions and leveraging sequential dependencies in both news headlines and descriptions, the proposed model demonstrated a 1% improvement, achieving an overall accuracy of 90%. These findings indicate that even a slight enhancement in modeling temporal dependencies and optimization can result in measurable gains in multi-class classification performance. The sequential combination of news headlines and descriptions is shown to be an effective strategy for capturing contextual features that improve the model’s predictive accuracy. This research contributes to the field of natural language processing by highlighting the effectiveness of sequential modeling and optimization in neural network-based text classification systems.
Klasifikasi Menggunakan Metode Random Forest untuk Awal Deteksi Diabetes Melitus Tipe 2 Iskandar, Reza Fauzan Nur; Gutama, Deden Hardan; Wijaya, Dhina Puspasari; Danianti, Dita
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 7 No. 3 (2024): July
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v7i3.26916

Abstract

Type 2 diabetes mellitus (T2DM) is a chronic disease with increasing prevalence. Early detection of DMTP2 is crucial in managing and preventing this disease. In this study, we propose the use of Random Forest method for early classification of T2DM based on risk factors. The dataset was obtained from UPTD Puskesmas Jatiroto with a total of 1111 data with 6 attributes of DMTP2 factors and 1 label. In the pre-processing stage, initial data processing includes cleaning missing values, feature engineering, and separation of training and test data. Next, the Random Forest model is trained using data that has been validated using K-Fold Cross Validation. Experimental results show that the proposed model produces an average accuracy of each fold of 97%. The final stage of evaluating the model by calculating precission, recall, and F1-Score, respectively, obtained results of 95%, 97%, and 96%. Model evaluation focuses on predicted labels so that the model can predict well in the case of DMTP2 problems based on similar data configurations.
Co-Authors Adhien Kenya Anima Estetikha Aditya, Muhammad Fahrul Agnum Ika Wulandari Amalia, Ade An-Nisa Apriani Andari, Rachma Yuni Andi Wahyudi Andri Pramuntadi Andri Pramuntadi Anni Karimatul Fauziyah Avrillaila Akbar Harahap Dadang Heksaputra Dadang Heksaputra Dadang Heksaputra Deden Hardan Gautama Deden Hardan Guatama Deviani Titi Nautami Dita Danianti Dita Danianti Doni Harisandi Emiliana, Emiliana Fariz Abdillah Haq Fatma Aidha Tatuhey Febriani, Hilda Amalia Gutama, Deden Hardan Gutama, Deden Hardan Handy Eka Putra Anwar Heksaputra, Dadang Imam Ihsani Indrayana, Sofyan Insih Wilujeng Iskandar, Reza Fauzan Nur Iskarini, Ariska istiqomah istiqomah izzah, lathifatul Izzati Muhimmah KHOIRUL ANAM Liansya Ayu Sariperkasi M. J. U. Haris Bahrudin Mega Purnama Sari Muhamad Irfanudin Muhammad Asman Muhammad Nur Wangid Musthofa Galih Pradana Ngidoti Musonah Nurona, Atmim Nurul Aulia Safitri Oktaviana, Agfa Permana, Candra Pramuntadi, Andri Prastowo, Wahit Desta Pujo Hari Saputro Putri, Mufida Awalia Qomaruzaman, Salis Nizar Ragil Satria Wicaksana Rahadian Kurniawan Ramadan, Muchammad Ilham Rahmad Rianita, Risti Nindi Riskiah, Putri Maulidatur Setiawan P, Andi Sri Kusumadewi Sri Nilawati Syifa Atikah Ardi Vita Aulia Rizki Wahyu A, Nova Laila Wasilaturrohmah Wasilaturrohmah Yahya, Abdullah Yanuar Wicaksono Yusinta Dwi Ariyani