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INDONESIA
Jurnal Ilmiah Sistem Informasi
Published by Universitas Muhammadiyah Metro
ISSN : 28295420     EISSN : 28294556     DOI : https://doi.org/10.24127
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Library & Information Science Other
Jurnal Ilmiah Sistem Informasi yang disingkat dengan JISI adalah salah satu Jurnal yang ada di Prodi D-III Sistem Informasi Fakultas Ilmu Komputer Universitas Muhammadiyah Metro Lampung Jurnal ini akan membahas mengenai beberapa permasalahan diantaranya yaitu Information System, Management Information System, Knowledge Management System, Project Management System, Geographic Information System, Supply Chain Management, Customer Relationship Management, Artificial Intelligence, Decision Support Systems, Software Engineering & Software Re-Engineering, Data Management, Data Mining, Cloud Computing dan Internet OF Things
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 46 Documents
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Analisis Sentimen Isu Megathrust Indonesia Di Twitter Menggunakan Support Vector Machine Dan Naive Bayes Christanto, Arya; Timur Samuel, Yusran
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 4 No. 1 (2025): MARET
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v4i1.8453

Abstract

Indonesia berisiko tinggi mengalami gempa megathrust yang berpotensi menimbulkan bencana besar, termasuk tsunami. Ancaman ini mendapat perhatian karena dapat merusak infrastruktur, mengganggu komunikasi, dan berdampak signifikan terhadap perekonomian. Media sosial, terutama Twitter, merupakan wadah setiap individu atau masyarakat dalam bertukar informasi, menyampaikan ceramah, dan memberikan pendapat terkait isu ini. Penelitian ini menganalisis sentimen publik di Twitter mengenai megathrust di Indonesia dengan metode algoritma Support Vector Machine (SVM) serta Naive Bayes (NVB). Data dikumpulkan dari 404 tweet berbahasa Indonesia yang diposting antara 1 Agustus hingga 30 September 2024. Setelah melalui pra-pemrosesan, data yang diperoleh dilabeling secara manual dan otomatis sebelum diklasifikasikan dengan RapidMiner. Hasil pada penelitian ini menjelaskan bahwa Naive Bayes memiliki tingkat akurasi yang lebih tinggi (83,58%) dibandingkan SVM (75,94%). Selain itu, NVB lebih unggul dalam mengenali sentimen negatif dengan recall sebesar 68%. Analisis tersebut memberikan dampak terbaru, terutama wawasan mengenai persepsi individua tau masyarakat terhadap megathrust dan dapat menjadi dasar dalam merancang strategi komunikasi yang lebih efektif. Dengan memahami respons masyarakat, pihak yang berwenang dapat menyusun kebijakan mitigasi bencana yang lebih tepat dan meningkatkan kesiapsiagaan masyarakat. Penelitian ini juga menyoroti pentingnya media sosial sebagai sumber data dalam kajian kebencanaan, khususnya dalam memahami reaksi dan kesiapan masyarakat terhadap ancaman gempa bumi
Pengembangan Website Penjualan W’D Cakes & Cookies dengan Integrasi CRM dalam Meningkatkan Loyalitas Pelanggan Renaldi Yulvianda; Muhammad Ismail; Mochammad Arief Hermawan Sutoyo
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 4 No. 1 (2025): MARET
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v4i1.8491

Abstract

The food and beverage industry, particularly the bakery and pastry sector, is growing rapidly due to increasing consumer demand and changes in consumption behavior. MSMEs like W’D Cakes & Cookies face operational challenges because they still rely on manual ordering systems via WhatsApp, which are prone to recording errors, service delays, and inefficient customer data management. This study aims to develop a web-based sales system integrated with Customer Relationship Management (CRM) to improve operational efficiency and customer loyalty. The research applies the Research and Development (R&D) method using the waterfall model, which includes requirement analysis, system design using Figma, development using the Laravel framework, and testing through Black Box Testing. The system implementation supports online ordering, product and customer management, digital payments, and transaction tracking. The embedded CRM features enable loyalty analysis, personalized promotions, and targeted customer communications. This system contributes to the digital transformation of MSMEs, increasing transaction speed, data accuracy, and service quality. Future research may explore integration with e-commerce platforms, chatbot implementation for customer service automation, and the use of artificial intelligence (AI) analytics to support data-driven marketing strategies.
Penerapan Metode SAW dalam Menentukan Prioritas Penerima Bantuan Sosial Budi Pratomo, Arief
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 4 No. 1 (2025): MARET
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v4i1.8556

Abstract

Penyaluran bantuan sosial sering kali menghadapi kendala dalam menentukan penerima yang benar-benar layak. Proses seleksi yang tidak objektif dapat menimbulkan ketidakadilan dan penyimpangan data. Penelitian ini bertujuan untuk membangun sistem pendukung keputusan (SPK) menggunakan metode Simple Additive Weighting (SAW) dalam menentukan prioritas penerima bantuan sosial. Metode SAW dipilih karena mampu mengolah data dengan banyak kriteria dan menghasilkan peringkat secara akurat. Kriteria yang digunakan dalam penelitian ini meliputi penghasilan per bulan, jumlah tanggungan, kondisi tempat tinggal, dan status pekerjaan. Sistem ini diimplementasikan dalam bentuk aplikasi berbasis web yang dapat membantu pihak berwenang dalam membuat keputusan secara objektif dan transparan.
Implementasi Metode Design Thinking dalam Perancangan UI/UX Aplikasi Menabung Arthalia Wulandari, Ika; Sukmasetya, Pristi; Mujito; Asmanto, Budi
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 4 No. 1 (2025): MARET
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v4i1.8560

Abstract

Mobile-based savings apps offer convenience in financial planning and management.However, many apps still face challenges regarding user experience (UI/UX) quality, which impacts user adoption and satisfaction. Poor user experience is often caused by interfaces that are not user-centered and a lack of understanding of users' needs. To address this issue, the Design Thinking approach, which focuses on empathy and innovative solutions,has become an increasingly popular methodology in application design. This study examines the application of the Design Thinking method in designing the UI/UX of savings apps, focusing on creating an intuitive and easy-to-use interface. The development process follows five main stages: Empathize, Define, Ideate, Prototype, and Test. The results of applying this method show that the Empathize stage helps developers understand users'saving habits and challenges.In contrast, the prototype stage allows for testing design solutions that better align with user expectations. This study also developed a web-based learning application prototype called "Nyelengi", which aims to assist various groups in managing personal finances. The prototype was evaluated using the System Usability Scale (SUS) to measure the quality of the user experience. The SUS calculation result scored 82,indicating reasonable user satisfaction with the app's interface and functionality. This score suggests that the app design produced through the Design Thinking approach effectively provides a user-friendly, intuitive, and easy-to-use experience. This study indicates that the application of Design Thinking can enhance user satisfaction, promote wider adoption of financial technology, and contribute to the development of more innovative and user-responsive applications
Sistem Pendukung Keputusan untuk Rekomendasi Olahraga Bedasarkan Kondisi KesehatanMenggunakan Metode Analythical Hierarchy Process (AHP) Kemalasari Siregar, Gunayanti; Pujianto; Ridhawati, Eka
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 4 No. 1 (2025): MARET
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v4i1.8561

Abstract

Exercise plays a vital role in maintaining physical health. However, selecting inappropriate types of exercise without considering an individual’s physical condition and medical history may lead to injury or worsen existing health problems. This study aims to develop a decision support system (DSS) that provides exercise recommendations based on the user's health condition. The Analytical Hierarchy Process (AHP) method is applied to weigh criteria according to their relative importance. The criteria considered in this study include age,body mass index (BMI), medical history, and physical fitness level. The exercise alternatives analyzed are running, cycling, swimming, yoga, and brisk walking. The AHP calculation results show that certain health conditions are more suitable for specific types of exercise,each with different priority weights. The proposed DSS can provide personalized and targeted exercise recommendations aligned with the user's condition. This system is expected to assist individuals in making safe and effective exercise decisions
Segmentasi Pelanggan Berdasarkan Volume Pembelian Produk Menggunakan Algoritma K-Means Untuk Efektivitas Pemasaran Kurnia, Dennis Ma'rifatul; Aulia, Nurun Nihayatur Rifqiya; Hidayah, Yulistiya Nur; Saputri, Cindy Avitaselly Bambang
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 4 No. 2 (2025): OKTOBER
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v4i2.9042

Abstract

The rapid development of information technology has changed the way products are sold, especially through online platforms that are increasingly in demand. In increasingly tight business competition, companies need to understand the differences in customer needs and behavior. Inability in this regard can make it difficult to design effective marketing strategies. Therefore, customer segmentation based on transaction data is an important solution to group customers based on similar purchasing patterns. This study aims to examine customer segmentation based on sales transactions to help companies understand customer characteristics and develop more targeted and adaptive marketing strategies. A quantitative approach is used by applying the K-Means Clustering algorithm and PCA dimension reduction to a dataset from Kaggle containing 3,900 entries with 9 attributes. Determination of the optimal number of clusters was carried out using the Elbow and Silhouette Score methods. The segmentation results show five optimal clusters with the highest Silhouette Score of 0.81. Cluster 0 is the most dominant. PCA visualization shows a fairly clear cluster separation although there is little overlap. This study has succeeded in grouping customers based on purchase volume. Limitations of the study include the uneven distribution of clusters and it is recommended to add demographic attributes and evaluate other algorithms such as DBSCAN.
Analisis Faktor Kepuasan Pengguna Silayak UIN Raden Fatah Palembang Menggunakan Model EUCS Muhammad Bayu Deswara; Rusmala Santi; Reni Septiyanti
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 4 No. 2 (2025): OKTOBER
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v4i2.9830

Abstract

Kesuksesan penerapan teknologi informasi di suatu institusi atau organisasi dapat diukur melalui tingkat kepuasan pengguna sistem informasinya. Di UIN Raden Fatah Palembang, Silayak (Sistem Layanan Akademik) telah diadopsi sebagai platform bagi mahasiswa untuk mengakses berbagai layanan akademik. Penelitian ini bertujuan untuk mengevaluasi pengaruh variabel dalam model End User Computing Satisfaction (EUCS), yang meliputi lima dimensi ialah Conten, Accuracy, Format, Ease of Use, dan Timeliness, serta satu variabel tambahan yaitu Security, terhadap kepuasan pengguna Silayak di lingkungan UIN Raden Fatah Palembang. Pendekatan yang di gunakan dalam penelitian ini ialah kuantitatif, dengan teknik pengumpulan data melalui kuesioner. Populasi dalam penelitian ini terdiri dari 21.387 mahasiswa aktif, dan sampel yang diambil sebanyak 381 responden menggunakan metode Simple Random Sampling, berdasarkan tabel penentuan jumlah sampel dengan tingkat signifikansi 5%. Hasil analisis menunjukkan bahwasanya lima variabel yaitu content, format, ease of use, timeliness, dan security memiliki pengaruh signifikan terhadap kepuasan pengguna, sedangkan variabel accuracy tidak memberikan pengaruh
Implementasi MobileNetV2 Untuk Pengenalan Presisi Penyakit Daun Kopi Berbasis Citra Anggraini, Selvi Fitria; Nafi’iyah, Nur; Qomariyah N, Nur
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 4 No. 2 (2025): OKTOBER
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v4i2.9876

Abstract

ABSTRACT Coffee is one of Indonesia’s leading commodities, playing a vital role in the national economy, providing employment opportunities, and serving as a primary source of income for many farmers. However, coffee productivity is often reduced due to pest and disease attacks, particularly on th leaves, such as coffee leaf rust and red spider mites. These diseases can disrupt photosynthesis, lowe plant quality, and even cause plant death if not addressed promptly. Manual identification at the farmer level is often challanging due to limited knowledge and the similarity of visual symptoms betwen diseases. This study aims to develop an image classification system for detecting healthy leaves, leaf rust, and red spider mite infestations on coffee plants automatically. The method employed is machine learning based on a Convolutional Neural Network architecture using MobileNetV2 and transfer learning. The dataset consists of 501 images of coffee leaves, divided into 456 training data and 45 testing data. The model was trained to distinguish between the three classes, achieving a training accuracy of 64% and a testing accuracy of 56%. The resulting model was then integrated into a web-based application using Streamlit, enabling easy access for farmers and the general public. This system is expected to facilitate early detection of coffee leaf diseases in a faster, more practical, and affordable way, allowing farmers to take timely action before damage spreads. In the long term, this technology is anticipated to support improved coffee plantation productivity in Indonesia. Keywords: image classification, coffee leaf, MobileNetV2, transfer learning.
Implementasi Implementasi Cnn Berbasis Deep Learning Untuk Klasifikasi Penyakit Daun Jagung Askan, Valentiena Prastika Putrie; Nafi’iyah, Nur
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 4 No. 2 (2025): OKTOBER
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v4i2.9885

Abstract

This study aims to develop and evaluate a convolutional neural network (CNN)-based model for classifying corn leaf diseases using a simple yet effective architecture. Four disease classes were considered: healthy, gray leaf spot, leaf blight, and common rust. A dataset comprising 13,136 images was obtained from the open-source PlantVillage Dataset and processed using class balancing techniques to mitigate prediction bias. Each image was resized to 256×256 pixels, normalized, and split into training (80%) and testing (20%) sets. The proposed CNN architecture consists of four convolutional layers with progressively increasing filters (16, 32, 64, 128), followed by max pooling, dropout, and two fully connected layers. The model was trained for 50 epochs using the Adam optimizer with categorical cross-entropy as the loss function. Performance evaluation, based on accuracy, precision, recall, and F1-score, achieved an accuracy of 97.18% with consistently high metrics across all classes. The results were further visualized using a confusion matrix and classification report. Finally, the trained model was deployed in a Flask-based web application, enabling users to upload corn leaf images and receive automated detection results. These findings demonstrate that a simple CNN architecture can achieve high accuracy in classifying corn leaf diseases and holds significant potential for integration into digital plant disease monitoring systems.
Penerapan Metode AHP Dan MFEP Dalam Menentukan Penerima Bantuan Benih Padi Nur Jamiyyah; M. Fakhriza; Muhammad Dedi Irawan
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 4 No. 2 (2025): OKTOBER
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v4i2.10066

Abstract

Penyaluran bantuan benih padi merupakan salah satu program pemerintah dalam meningkatkan produktivitas pertanian dan ketahanan pangan. Namun, proses seleksi penerima bantuan sering mengalami kendala karena keterbatasan kuota dan banyaknya kelompok tani yang mengajukan permohonan. Penelitian ini bertujuan untuk membangun sistem pendukung keputusan (SPK) yang dapat membantu menentukan penerima bantuan secara objektif dan tepat sasaran. Metode yang digunakan adalah kombinasi Analytical Hierarchy Process (AHP) untuk menentukan bobot setiap kriteria, dan Multi Factor Evaluation Process (MFEP) untuk menghitung skor dan peringkat setiap alternatif. Kriteria yang digunakan meliputi terdaftar di simluhtan, mengajukan proposal, produktivitas lahan, luas lahan, dan status penerimaan bantuan sebelumnya. Hasil pengujian menunjukkan bahwa sistem mampu memberikan rekomendasi yang sesuai dengan kebijakan penyaluran bantuan. Sistem ini dibangun menggunakan bahasa pemrograman PHP dan basis data MySQL. Implementasi sistem ini diharapkan dapat meningkatkan transparansi, efisiensi, dan keadilan dalam proses pemberian bantuan benih padi.

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