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IPSIKOM
Published by Universitas Insan Pembangunan Indonesia
ISSN : 23384093     EISSN : 26866382     DOI : https://doi.org/10.58217
Core Subject : Science,
Computer Science & IT
Jurnal Insan Pembangunan Sistem Informasi dan Komputer (IPSIKOM) diterbitkan oleh Fakultas Ilmu Komputer (FILKOM), Universitas Insan Pembangunan Indonesia sejak Juni 2013. IPSIKOM memuat naskah hasil-hasil penelitian di bidang Sistem Informasi dan Ilmu Komputer. IPSIKOM berkomitmen untuk memuat artikel berbahasa Indonesia yang berkualitas dan dapat menjadi rujukan utama para peneliti dalam bidang Sistem Informasi dan Ilmu Komputer. Scope Jurnal IPSIKOM Big Data Artificial Intelligence Information system Accounting System Information Education Technology Information Security information Data mining Application Information System Business Intelligence Decision Support Systems Intelligent Systems Machine Learning Network and Computer Security Optimization Pattern Recognition Soft Computing Software Engineering Information Technologi
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 268 Documents
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RANCANG BANGUN APLIKASI ANALISIS SENTIMEN ULASAN APLIKASI DI GOOGLE PLAY STORE MENGGUNAKAN METODE LOGISTIC REGRESSION DAN LEXICON-BASED APPROACH Fauzan Fadhillah Arisandi
IPSIKOM Vol. 13 No. 2 (2025): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v13i2.390

Abstract

Dalam era digital yang semakin maju, ulasan pengguna terhadap aplikasi seluler di platform seperti Google Play Store menjadi sumber informasi penting bagi pengembang maupun pengguna aplikasi. Ulasan ini dapat mencerminkan kepuasan, keluhan, dan harapan pengguna terhadap sebuah aplikasi. Namun, karena volume ulasan yang terus meningkat, sangat sulit bagi pengembang untuk membaca dan mengevaluasi seluruh opini secara manual. Di sisi lain, ulasan yang tersedia sering kali tidak mencerminkan kondisi sebenarnya akibat adanya ulasan palsu, bias, atau manipulatif. Oleh karena itu, dibutuhkan sistem yang otomatis dapat mampu mengelompokkan ulasan berdasarkan polaritas sentimen dengan akurat. Penelitian ini bertujuan untuk merancang dan membangun sebuah aplikasi berbasis web menggunakan framework Streamlit yang dapat melakukan analisis sentimen terhadap ulasan aplikasi di Google Play Store. Metode yang digunakan adalah kombinasi antara Logistic Regression dan Lexicon-Based Approach dengan tahapan preprocessing teks seperti cleansing, tokenizing, stemming, hingga transformasi menggunakan TF-IDF. Penelitian ini menggunakan pendekatan CRISP-DM dalam membangun model dan sistem aplikasi. Hasil akhir dari penelitian ini adalah sebuah aplikasi analisis sentimen yang mampu mengklasifikasikan ulasan ke dalam kategori positif dan negatif, serta menampilkan hasil evaluasi model dalam bentuk visualisasi metrik dan word cloud. Dari studi kasus aplikasi Dana, berdasarkan hasil evaluasi performa model dengan metrik confusion matrix, model Logistic Regression yang dibangun mencapai accuracy sebesar 91,2%, precision 91,73%, recall 98,46%, dan F1-score 64,78%.
PENGARUH PENGGUNAAN APLIKASI BACAAN DIGITAL TERHADAP MINAT BACA SISWA MENENGAH PERTAMA (SMP) Nur Adhira; Dian Fadillah; Pajar Aswad; Juniarti Iryani
IPSIKOM Vol. 13 No. 2 (2025): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v13i2.427

Abstract

Penelitian ini bertujuan untuk mengetahui pengaruh penggunaan aplikasi bacaan digital terhadap minat baca siswa Sekolah Menengah Pertama (SMP) di Kabupaten Bulukumba. Penelitian ini menggunakan pendekatan kuantitatif deskriptif dengan teknik pengumpulan data melalui kuesioner. Data dianalisis menggunakan perhitungan rata-rata dan persentase terhadap indikator minat baca yang meliputi frekuensi membaca, durasi membaca, dan ketertarikan terhadap konten. Hasil penelitian menunjukkan bahwa sebagian besar siswa telah menggunakan aplikasi bacaan digital seperti iPusnas, Let’s Read, dan e-Perpus. Responden memberikan tanggapan positif terhadap kemudahan akses, keberagaman konten, dan fitur interaktif dari aplikasi tersebut. Skor tertinggi terdapat pada indikator ketertarikan terhadap konten, diikuti oleh frekuensi dan durasi membaca. Tingginya persentase siswa yang menyatakan setuju dan sangat setuju menunjukkan bahwa aplikasi bacaan digital memberikan kontribusi positif terhadap kebiasaan membaca mereka. Implikasinya, aplikasi bacaan digital dapat menjadi media literasi yang efektif dalam mendorong minat baca siswa, kususunya di Kabupaten Bulukumba. Sekolah dapat memanfaatkan aplikasi ini sebagai alternatif pembelajaran yang sesuai dengan karakteristik generasi digital. Penelitian ini diharapkan menjadi referensi dalam pengembangan program literasi berbasis teknologi secara berkelanjutan.
ANALISIS SENTIMEN TERHADAP KURIKULUM MERDEKA MENGGUNAKAN VADER DAN LSTM (STUDI BERBASIS TWITTER) Friana Widya Gunawan; Novita Lestari Anggreini
IPSIKOM Vol. 13 No. 2 (2025): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v13i2.428

Abstract

This study explores public sentiment toward the Merdeka Curriculum, a policy reform introduced to address educational challenges in Indonesia following the COVID-19 pandemic. Utilizing Twitter (now X) as a data source, this research collected 1,900 Indonesian-language tweets to assess how society perceives the curriculum’s flexibility and relevance. The analysis begins with data preprocessing, including cleaning, tokenization, stopword removal, and stemming. VADER (Valence Aware Dictionary and Sentiment Reasoner) is employed for initial sentiment labeling, classifying tweets into positive, neutral, and negative categories. The results reveal a significant class imbalance: 1,692 neutral, 117 positive, and 91 negative tweets. These labeled tweets are further analyzed using the Long Short-Term Memory (LSTM) algorithm for deep sentiment classification. The LSTM model demonstrates training accuracy improvements from 82% to 90.29% across five epochs, while validation accuracy remains steady at 88%. However, the model fails to accurately classify minority classes, with precision, recall, and F1-scores for positive and negative sentiments scoring zero. This indicates that while LSTM can effectively recognize the dominant neutral sentiment, it struggles with minority class identification due to data imbalance. The findings highlight the need for improved model training strategies and data augmentation to enhance classification performance across all sentiment categories. Overall, this study contributes to the integration of lexicon-based and deep learning approaches for sentiment analysis and offers valuable insights for educators and policymakers in optimizing the Merdeka Curriculum through data-informed decisions.
ANALISIS SENTIMEN MASYARAKAT TERHADAP PINJAMAN ONLINE DI APLIKASI X MENGGUNAKAN LONG SHORT-TERM MEMORY Hafizh Maalik Falah; Castaka Agus Sugianto
IPSIKOM Vol. 13 No. 2 (2025): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v13i2.429

Abstract

The development of online loans in Indonesia has led to various public opinions spread across social media, one of which is the X platform. This research aims to analyze public sentiment towards online loans using the Long Short-Term Memory (LSTM) method. The data used consists of 702 Indonesian tweets collected through a crawling process with Tweet Harvest. Of these, 480 tweets were classified as positive sentiment and 222 as negative. The research process includes preprocessing, manual labeling, model training, and evaluation stages. The model was built using Sequential architecture from Keras, consisting of embedding layer, LSTM layer 128 units, 30% dropout, and output layer with softmax activation function. The model was trained using 562 tweets as training data and 140 tweets as validation data with a ratio of 80:20, for 10 epochs and batch size 64. The final evaluation using the entire dataset resulted in 92.59% accuracy, with 79.06% precision, 79.43% recall, and 79.14% F1-score. These results show that LSTM is able to classify sentiment stably and effectively, and has strong potential in sentiment analysis on short text data such as tweets.
APLIKASI PERMAINAN KARTU MEMORI BERBASIS WEB UNTUK PEMBELAJARAN KOSAKATA BAHASA SUNDA (STUDI KASUS SEKOLAH DASAR NEGERI BINAWARGA CIPONGKOR) Ridwan Nur Hakim; Dini Rohmayani
IPSIKOM Vol. 13 No. 2 (2025): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v13i2.430

Abstract

In today's digital era, technology-based learning is an important need, especially in maintaining the use of regional languages such as Sundanese which are increasingly rarely used in everyday life. This study aims to design, implement, and test a web-based memory card game application as a medium for learning Sundanese vocabulary for elementary school students. The development method used is the waterfall model which includes the stages of analysis, design, implementation, testing, deployment, and maintenance. This application was developed using React with Tailwind CSS for the frontend, Node.js with Express JS, and PostgreSQL as the database. The main features provided include difficulty level selection, point system, leaderboard, and vocabulary and student data management by the admin. The results of black box testing all features have run 100% according to their functions, and the results of User Acceptance Testing (UAT) from 3 testing parameters namely features, design, and user satisfaction show a very positive response from teachers with a satisfaction level of 95.1% and from students of 74.6%. These findings show that the application can be well received by users and is able to increase students' interest in learning Sundanese language.
SISTEM ENKRIPSI FILE DENGAN ID-CARD MENGGUNAKAN LARAVEL Diva Ramadhan; Muhammad Imam Gunawan
IPSIKOM Vol. 13 No. 2 (2025): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v13i2.431

Abstract

The security of digital data has become crucial in today's era of information exchange. Many data breaches occur due to weak file protection in web-based systems. This research aims to design and develop a web-based file encryption system using the Advanced Encryption Standard (AES) algorithm, where file access is restricted only to users who possess a valid ID Card or QR Code as an authentication key. The software development approach adopted in this development is the Waterfall methodology, which follows a sequential process consisting of requirement analysis, system design, implementation, and testing phases. The software was built using the Laravel framework with key features such as automatic file encryption upon upload, folder management, and additional authentication through ID Card or QR Code scanning. Administrator has full access to manage users while regular users can only upload and manage their private files. The implementation result show that files were successfully encrypted and can only be accessed using a valid ID Card. The AES encryption process effectively secures file confidentiality, while the use of an ID Card enhances access security to the uploaded files. Therefore, this software can fulfills the research objective of improving file security in web-based systems.
APLIKASI PENINGKATAN KEMAMPUAN MEMBACA BERBASIS MULTIMEDIA INTERAKTIF MENGGUNAKAN CONSTRUCT 2 (STUDI KASUS SDN SINARJAYA) Devi Oktaviani; Wali Muhammad
IPSIKOM Vol. 13 No. 2 (2025): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v13i2.432

Abstract

Pendidikan memiliki peran penting dalam mengembangkan potensi individu dan mempersiapkan keterampilan berbahasa terutama membaca menjadi fondasi utama dalam proses belajar, khususnya bagi siswa Sekolah Dasar (SD). Berdasarkan observasi awal di SDN Sinarjaya, ditemukan bahwa sebagian besar siswa kelas 2 masih mengalami kesulitan dalam membaca dengan lancar. Penelitian ini bertujuan untuk merancang dan membuat aplikasi pembelajaran membaca berbasis multimedia interaktif menggunakan Construct 2. Aplikasi dirancang dengan fitur menarik seperti merangkai kata, komik digital dan fitur tambahan berupa suara dan kuis. Metode yang digunakan dalam pembuatan aplikasi game sederhana ini adalah MDLC (Multimedia Development Life Cycle) dengan 6 tahap yaitu konsep, perancangan, pengumpulan, pembuatan, testing dan pendistribusian. Hasil pembuatan aplikasi game ini menggunakan Construct 2, dimana tahap kegiatan sesuai dengan metode yang digunakan. Diharapkan aplikasi game berbasis multimedia interaktif ini dapat berkontribusi secara signifikan dalam menciptakan pembelajaran yang lebih baik dan relevan dengan kebutuhan siswa Sekolah Dasar era digital.
APLIKASI PELAPORAN BULLYING (DUCARE) BERBASIS WEB (STUDI KASUS DARUL ‘ULUM CIWIDEY-PASIRJAMBU) Encep Rahman Armana; Novita Lestari Anggreini
IPSIKOM Vol. 13 No. 2 (2025): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v13i2.433

Abstract

Bullying di lingkungan sekolah menjadi isu serius yang memerlukan penanganan cepat dan tepat. Penelitian ini merancang dan membangun aplikasi pelaporan bullying berbasis web bernama DUCARE yang diterapkan di Darul 'Ulum Ciwidey-Pasirjambu. Aplikasi ini memfasilitasi pelapor untuk menyampaikan laporan bullying secara anonim maupun non-anonim, serta mendukung tiga peran pengguna yaitu pelapor, guru BK, dan admin. Fitur-fitur utama meliputi form pelaporan, manajemen laporan, notifikasi, dan pelacakan status penanganan. Berdasarkan pengujian fungsional (Black Box) dan User Acceptance Test (UAT) dengan 15 responden, aplikasi memperoleh tingkat kepuasan sebesar 88,7%, yang menunjukkan bahwa aplikasi ini layak digunakan dan membantu meningkatkan efektivitas penanganan kasus bullying secara digital di lingkungan sekolah.
IMPLEMENTASI CONVOLUTIONAL NEURAL NETWORK (CNN) UN-TUK KLASIFIKASI TANAMAN HIAS BERBASIS APLIKASI WEB LARAVEL Mochammad Fadhilah Fajar; Aris Haris Rismayana
IPSIKOM Vol. 13 No. 2 (2025): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v13i2.434

Abstract

The automatic classification of ornamental plants plays a vital role in improving efficiency in garden management and digital agriculture. This study aims to develop an image-based ornamental plant classification system using the Convolutional Neural Network (CNN) method, integrated into a Laravel-based web application. The CNN model was trained on a dataset comprising over 14,000 images from 29 ornamental plant classes. Data augmentation techniques were applied to enhance the model’s generalization ability. The research process involved image preprocessing, CNN model training, performance evaluation using accuracy, precision, recall, and F1-score, and system deployment through a web interface developed with Laravel. The training results showed a validation accuracy of 77.38%. The deployed system is capable of real-time prediction based on user-uploaded plant images. In one of the tests, a rose image yielded a classification accuracy of 56%, indicating varying performance depending on plant class. These results suggest that integrating CNN with a Laravel-based web platform can provide a reliable tool for classifying ornamental plants. The system demonstrates potential use in household plant identification, gardening support, and plant education. Further improvements may include expanding the dataset, refining class balance, and optimizing model architecture to increase accuracy for specific plant types.
PERANCANGAN FITUR ANOTASI PADA CHATGPT MENGGUNAKAN EKSTENSI BROWSER Elza Satria Bhima Sakti; Dini Rohmayani
IPSIKOM Vol. 13 No. 2 (2025): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v13i2.435

Abstract

The rapid advancement of large language models such as ChatGPT has revolutionized text-based interactions, yet lacks built-in annotation mechanisms for marking, storing, and revisiting critical conversation points. This study designs and implements a browser extension using ReactJS and Tailwind CSS with local storage via IndexedDB, following a five-stage waterfall model encompassing requirements analysis, system design, implementation, testing (black-box and user acceptance), and maintenance. The extension supports CRUD operations on annotations, automatic navigation to annotated text positions, and JSON import/export without disrupting ChatGPT’s original interface. Testing demonstrates full compliance with functional specifications, while a UAT with 11 participants reports average satisfaction scores ≥ 4.55 for usability, UI coherence, and review efficiency, confirming enhanced productivity and learning experience. In conclusion, the extension effectively facilitates critical thinking and collaboration in web-based learning, with future work exploring cross-device synchronization, real-time collaboration, and AI-driven annotation suggestions to further optimize user engagement.

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