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HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi
Published by Sekolah Tinggi Manajemen Informatika Komputer Uyelindo Kupang
ISSN : 23375280     EISSN : 26207427     DOI : 10.52972
Core Subject : Science,
Computer Science & IT
Jurnal Jurnal High education of organization archive quality Teknologi Informasi merupakan Jurnal Ilmiah untuk menampung hasil penelitian yang berhubungan dengan bidang sains dan teknologi. Bidang penelitian yang dimaksud meliputi : Artificial Intelligence and Application, Business Intelligence, Cloud and Grid Computing, Computer Networking & Security, Computer-Based Multimedia Retrievel, Datawarehouse & Data Mining, Decision Support System, Enterprise System,(SCM, ERP, CRM), E-System (E-Business, E-Commerce, E-Government, E-Health), Expert & Knowledge-Based System, Fuzzy Logic, Genetic Algorithms, Geographics Information System, Human-Computer Interaction, Image Processing, Information Retrieval, Information System, IT Governance, Knowledge Management, Mobile Computing & Application, Multimedia System, Neural Networks, Open Source System & Technology, Pattern Recognition, Semantic Web, Software Engineering
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 114 Documents
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SISTEM INFORMASI AKADEMIK BERBASIS WEB PADA SD INPRES KODE: WEB-BASED ACADEMIC INFORMATION SYSTEM FOR SD INPRES KODE Nurak, Fransiska Gervina Dua; Chandra, Conchita Junita; Rozady, Margaretha Paulina Novianty
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 16 No. 2 (2025): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol16no2.p202-212

Abstract

Perkembangan teknologi informasi secara tidak langsung mengharuskan berbagai bidang untuk beradaptasi dengan perubahan yang ada, termasuk bidang pendidikan. SD Inpres Kode masih mengelola data akademik secara manual, yang rentan terhadap kehilangan data, ketidakefisienan dalam pengelolaan presensi, serta menyulitkan penyesuaian jadwal dan penilaian sesuai Kurikulum Merdeka. Sebagai solusi, dibangun sistem informasi akademik berbasis web yang dirancang untuk mengelola data akademik secara lebih efektif, efisien, dan aman. Sistem dibangun menggunakan model Modified Waterfall, dengan bahasa pemrograman PHP  dan DBMS MySQL. Hasil pengujian sistem yang meliputi uji fungsionalitas, uji performa (menggunakan GTMetrix, Lighthouse, PageSpeed Insight, dan Apache JMeter), dan uji kompatibilitas perangkat menunjukkan bahwa seluruh fitur berjalan dengan baik dan responsif di berbagai perangkat dan browser. Sementara itu, uji kepuasan pengguna yang melibatkan berbagai pihak menunjukkan tingkat kepuasan yang tinggi, yaitu admin (57,1% setuju; 42,9% sangat setuju), wali kelas (9,5% netral; 64,3% setuju; 26,2% sangat setuju), guru mata pelajaran (86,7% setuju; 13,3% sangat setuju), kepala sekolah (33,3% setuju; 66,7% sangat setuju), ketua gugus (40% setuju; 60% sangat setuju), dan pengunjung (4,1% netral; 42,4% setuju; 53,5% sangat setuju). Hasil ini mencerminkan kepuasan tinggi terhadap kemudahan penggunaan, kecepatan akses, dan kelengkapan fitur yang disediakan. Sistem ini terbukti dapat meningkatkan efisiensi pengelolaan data akademik serta mendukung implementasi Kurikulum Merdeka secara optimal.   Advances in information technology indirectly require various fields to adapt to these changes, including education. Inpres Elementary School of Kode still manages academic data manually, which is prone to data loss, inefficiencies in attendance management, and makes it difficult to adjust schedules and assessments according to the Merdeka Curriculum. As a solution, a web-based academic information system was built which was designed to manage academic data more effectively, efficiently and securely. The system is built using the Modified Waterfall model, with the PHP programming language and MySQL DBMS. The results of system testing, which include functionality testing, performance testing (using GTmetrix, Lighthouse, Page Speed Insight, and Apache JMeter), and device compatibility testing, show that all features run well and are responsive on various devices and browsers. Meanwhile, user satisfaction tests involving various parties showed a high level of satisfaction, namely admin (57.1% agree; 42.9% strongly agree), homeroom teachers (9.5% neutral; 64.3% agree; 26.2% strongly agree), subject teachers (86.7% agree; 13.3% strongly agree), principals (33.3% agree; 66.7% strongly agree), group leaders (40% agree; 60% strongly agree), and visitors (4.1% neutral; 42.4% agree; 53.5% strongly agree). These results reflect high levels of satisfaction with the ease of use, speed of access, and comprehensiveness of the features provided. This system is expected to improve the efficiency of academic data management and support the optimal implementation of the Merdeka Curriculum.
ANALISIS SENTIMEN KOMENTAR BERPOTENSI TOXIC PADA MEDIA SOSIAL TIKTOK MENGGUNAKAN METODE DECISION TREE: SENTIMENT ANALYSIS OF POTENTIALLY TOXIC COMMENTS ON TIKTOK SOCIAL MEDIA USING THE DECISION TREE METHOD Jasno, Bobbin Ariyadi; Fathoni, Ahmad Ariful; David; Putra, Dwiki Dharma; Hasan, Mohammad Zidane; Amsury, Fachry; Supendar, Hendra
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 16 No. 2 (2025): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol16no2.p193-201

Abstract

Penelitian ini bertujuan untuk mengatasi tantangan dalam mendeteksi dan mengklasifikasikan komentar berpotensi toxic secara otomatis pada media sosial TikTok, yang dikenal padat dengan bahasa informal, slang, dan cyber-aggression, menggunakan Analisis Sentimen dengan algoritma Decision Tree. Dataset yang digunakan terdiri dari 271 komentar primer yang dikumpulkan langsung dari feed video TikTok dan diklasifikasikan secara seimbang ke dalam kategori Toxic (Label = 1) dan Non-Toxic (Label = 0). Tahapan metodologi mencakup normalisasi bahasa slang TikTok, preprocessing teks, dan pembobotan fitur menggunakan Term Frequency–Inverse Document Frequency (TF-IDF) untuk menonjolkan fitur linguistik yang berkaitan dengan toksisitas. Hasil pengujian menunjukkan bahwa model menghasilkan akurasi sebesar 0,75, precision untuk kelas toxic sebesar 0,787, dan recall sebesar 0,765, sehingga mencerminkan performa aktual model dalam mendeteksi komentar toxic setelah proses preprocessing dan TF-IDF. Teknik post-pruning turut membantu mengurangi overfitting dan meningkatkan kemampuan generalisasi model terhadap data baru, meskipun penelitian ini tidak melakukan pengujian formal terhadap efisiensi komputasi maupun keandalan sistem. Secara keseluruhan, kombinasi normalisasi slang, TF-IDF, dan Decision Tree dengan post-pruning mampu menghasilkan performa klasifikasi yang stabil dalam identifikasi komentar toxic pada TikTok berbasis data primer.   This study aims to address the challenges of automatically detecting and classifying potentially toxic comments on the TikTok social media platform, which is characterized by heavy use of informal language, slang, and cyber-aggression, by applying Sentiment Analysis using the Decision Tree algorithm. The dataset consists of 271 primary comments collected directly from TikTok video feeds and evenly categorized into Toxic (Label = 1) and Non-Toxic (Label = 0). The methodological stages include TikTok-specific slang normalization, text preprocessing, and feature weighting using Term Frequency–Inverse Document Frequency (TF-IDF) to highlight linguistic features associated with toxicity. Experimental results show that the model achieves an accuracy of 0.75, a precision of 0.787, and a recall of 0.765 for the toxic class, reflecting the model’s actual performance after preprocessing and TF-IDF optimization. The application of post-pruning also helps reduce overfitting and improves the model’s generalization ability toward new data, although the study does not conduct formal evaluations of computational efficiency or system reliability. Overall, the combination of slang normalization, TF-IDF, and a pruned Decision Tree demonstrates stable classification performance in identifying toxic comments on TikTok based on the primary data used.
KAJIAN PENERAPAN BLOCKCHAIN PADA SISTEM AKADEMIK DI PERGURUAN TINGGI: SEBUAH LITERATURE REVIEW: A STUDY ON THE APPLICATION OF BLOCKCHAIN IN ACADEMIC SYSTEMS IN HIGHER EDUCATION: A LITERATURE REVIEW Ndaumanu, Ricky Imanuel
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 16 No. 2 (2025): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol16no2.p186-192

Abstract

Penelitian ini mengkaji penerapan teknologi blockchain dalam sistem akademik di perguruan tinggi sebagai respons terhadap meningkatnya kebutuhan akan keamanan, transparansi, dan efisiensi dalam pengelolaan data akademik. Tujuan utama penelitian ini adalah menganalisis secara sistematis bagaimana blockchain telah digunakan, sedang diterapkan, dan berpotensi diintegrasikan dalam berbagai proses akademik, termasuk verifikasi kredensial, pengelolaan rekam jejak belajar, serta interoperabilitas data antar-institusi. Menggunakan pendekatan systematic literature review, penelitian ini menerapkan protokol pencarian, seleksi, dan sintesis literatur secara ketat untuk mengidentifikasi publikasi relevan dalam lima tahun terakhir. Hasil kajian menunjukkan bahwa meskipun blockchain menawarkan manfaat strategis seperti imutabilitas data, desentralisasi, dan otomatisasi melalui smart contract, implementasi praktisnya dalam konteks perguruan tinggi masih terbatas pada verifikasi sertifikat dan belum banyak menjangkau keseluruhan siklus akademik. Temuan juga mengungkap adanya sejumlah tantangan, termasuk kesiapan infrastruktur, regulasi, dan kapabilitas institusional yang masih rendah, khususnya di negara berkembang. Penelitian ini memberikan kontribusi teoretis melalui integrasi konsep teknologi blockchain dengan karakteristik sistem akademik modern, serta kontribusi praktis berupa pemetaan peluang dan hambatan implementasi yang dapat menjadi acuan bagi institusi pendidikan tinggi dan pembuat kebijakan dalam merencanakan adopsi teknologi secara lebih terarah dan berkelanjutan.   This study examines the application of blockchain technology in academic systems at universities in response to the increasing need for security, transparency, and efficiency in academic data management. The main objective of this study is to systematically analyse how blockchain has been used, is being implemented, and has the potential to be integrated into various academic processes, including credential verification, learning record management, and inter-institutional data interoperability. Using a systematic literature review approach, this study applies strict literature search, selection, and synthesis protocols to identify relevant publications from the last five years. The results of the study show that although blockchain offers strategic benefits such as data immutability, decentralisation, and automation through smart contracts, its practical implementation in the context of higher education is still limited to certificate verification and has not yet reached the entire academic cycle. The findings also reveal a number of challenges, including infrastructure readiness, regulations, and low institutional capabilities, especially in developing countries. This research provides a theoretical contribution through the integration of blockchain technology concepts with the characteristics of modern academic systems, as well as a practical contribution in the form of mapping the opportunities and obstacles to implementation, which can be used as a reference for higher education institutions and policymakers in planning a more focused and sustainable adoption of the technology.
IMPLEMENTASI K-MEANS UNTUK PENENTUAN KEPUTUSAN PENJUALAN JUS (STUDI KASUS CAFÉ JUS XYZ): IMPLEMENTATION OF K-MEANS FOR JUICE SALES DECISION-MAKING (CASE STUDY: XYZ JUICE CAFÉ) Putra, Alfred Yulius Arthadi; Suarezsaga, Fredrikus; Kristina
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 16 No. 2 (2025): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol16no2.p247-253

Abstract

Usaha penjualan jus buah oleh Café Jus XYZ (nama usaha disamarkan) di Pontianak, Kalimantan Barat menghadapi persaingan yang ketat akibat banyaknya usaha serupa. Kondisi ini menuntut pemilik melakukan pengambilan keputusan berbasis data untuk menentukan prioritas promosi dan pengelolaan stok bahan baku. Penelitian ini memanfaatkan algoritma K-Means untuk mengelompokkan performa menu berdasarkan data transaksi penjualan yang diambil dari sistem kasir periode Juli–September 2025 sebanyak 1.250 transaksi. Data transaksi berbentuk daftar item per transaksi, sehingga dilakukan proses pemisahan item dan agregasi untuk memperoleh jumlah transaksi yang memuat tiap menu. Dari 13 menu yang tersedia, terdapat 10 menu yang tercatat terjual pada periode pengamatan dan digunakan dalam analisis. Hasil K-Means dengan k=3 menghasilkan tiga kategori, yaitu Penjualan Tinggi, Penjualan Sedang, dan Penjualan Rendah. Menu Mango menjadi satu-satunya menu pada kategori Penjualan Tinggi. Enam menu (Avocado, Green Tea, Melon, Melon Lychee, Red Guava, dan Watermelon) masuk kategori Penjualan Sedang, sedangkan tiga menu (Apple, Cookies n Cream, dan Orange) masuk kategori Penjualan Rendah. Selain menghasilkan klaster, kualitas klaster dievaluasi menggunakan metrik internal (WCSS, Silhouette, Davies-Bouldin, dan Calinski-Harabasz) untuk mendukung pemilihan jumlah klaster. Hasil klaster menjadi dasar rekomendasi promosi dan evaluasi menu bagi Café Jus XYZ.   The fruit juice sales business of Café Jus XYZ (business name anonymized) in Pontianak, West Kalimantan, faces intense competition due to the large number of similar businesses. This condition requires data-driven decisions to prioritize promotions and manage raw-material inventory. This study applies the K-Means algorithm to group menu performance using sales transaction data collected from the point-of-sale system during July–September 2025 (1,250 transactions). Each transaction contains a list of purchased items; therefore, the data are split into individual items and aggregated to obtain the number of transactions containing each menu. Although the café offers 13 menu variants, only 10 menus were sold during the observation period and were included in the analysis. The K-Means result with k=3 produces three categories: High, Medium, and Low Sales. Mango is the only menu item in the High Sales category. Six menu items (Avocado, Green Tea, Melon, Melon Lychee, Red Guava, and Watermelon) belong to the Medium Sales category, while three items (Apple, Cookies n Cream, and Orange) fall into the Low Sales category. In addition to clustering, cluster quality is evaluated using internal metrics (WCSS, Silhouette, Davies-Bouldin, and Calinski-Harabasz) to support the choice of the number of clusters. The clustering output is then used to derive recommendations for promotion and menu evaluation.

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