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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 126 Documents
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ANALISIS KESADARAN MAHASISWA TERHADAP PRIVASI DATA DENGAN MENGGUNAKAN METODE NAÏVE BAYES: ANALYSIS OF STUDENTS’ AWARENESS OF DATA PRIVACY USING THE NAÏVE BAYES METHOD Septia, Kaman; Fhadila, Loade Thoriq; Syahril, Muhammad Irvan; Sukarno, Chesario; Nazara, Iman Kasih; Amsury, Fachri
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 17 No. 1 (2026): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol17no1.p29-37

Abstract

Privasi data merupakan aspek penting dalam aktivitas digital, terutama bagi mahasiswa yang aktif menggunakan berbagai platform daring. Penelitian ini bertujuan menganalisis tingkat kesadaran privasi data mahasiswa menggunakan algoritma Naïve Bayes. Data primer dikumpulkan melalui kuesioner Google Form yang berisi 13 indikator kesadaran privasi dan disebarkan melalui media sosial dengan teknik voluntary response sampling. Sebanyak 56 mahasiswa berpartisipasi sebagai sampel penelitian. Pengolahan data mengikuti tahapan Knowledge Discovery in Database (KDD), meliputi seleksi data, pembersihan, transformasi, pemodelan, serta evaluasi. Transformasi dilakukan dengan menghitung skor total per responden dan mengelompokkan tingkat kesadaran ke dalam kategori “Tinggi” dan “Standar” menggunakan cut-off empiris untuk menjaga keseimbangan kelas. Analisis klasifikasi dilakukan menggunakan algoritma Naïve Bayes melalui aplikasi Orange Data Mining, dengan evaluasi menggunakan Test and Score serta Confusion Matrix. Hasil penelitian menunjukkan bahwa model mampu mengklasifikasikan tingkat kesadaran privasi dengan akurasi 91.1%, precision 92.6%, recall 91.1%, F1-score 91.5%, AUC 0.976, dan MCC 0.738. Temuan ini menunjukkan bahwa Naïve Bayes efektif dalam mengenali pola kesadaran privasi mahasiswa dan layak digunakan sebagai dasar pengembangan program edukasi privasi data di lingkungan perguruan tinggi.   Data privacy is a critical aspect of digital activity, particularly for university students who frequently engage with online platforms. This study aims to analyze students’ awareness of data privacy using the Naïve Bayes classification algorithm. Primary data were collected through a Google Form questionnaire consisting of 13 indicators of privacy awareness and distributed via social media using a voluntary response sampling technique. A total of 56 students participated in this study. Data processing followed the Knowledge Discovery in Database (KDD) stages, including data selection, cleaning, transformation, modeling, and evaluation. The transformation process involved calculating the total awareness score for each respondent and categorizing awareness levels into “High” and “Standard” using an empirical cut-off to maintain class balance. The Naïve Bayes algorithm was applied using the Orange Data Mining application, with performance evaluated through the Test and Score and Confusion Matrix tools. The results indicate that the model performed effectively, achieving an accuracy of 91.1%, precision of 92.6%, recall of 91.1%, F1-score of 91.5%, AUC of 0.976, and MCC of 0.738. These findings demonstrate that Naïve Bayes is suitable for analyzing student privacy awareness patterns and can serve as a foundation for designing educational interventions to improve privacy literacy in academic environments.
PENERAPAN ALGORITMA APRIORI UNTUK ANALISIS POLA PEMILIHAN MENU DI RH STORE: IMPLEMENTATION OF THE APRIORI ALGORITHM FOR ANALYZING MENU SELECTION PATTERNS AT RH STORE Pratama, Dimas Limanov; Kristy, Natasya; Saputra, Rayhan Daffananda; Ihsan, Muhammad Awaluddin Azhari; Amsury, Fachri; Supendar, Hendra
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 17 No. 1 (2026): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol17no1.p19-28

Abstract

Fluktuasi penjualan yang dialami RH Store menunjukkan perlunya pemanfaatan data transaksi secara optimal untuk mendukung pengambilan keputusan bisnis. Selama ini, data transaksi penjualan belum dimanfaatkan secara maksimal untuk mengidentifikasi pola pemilihan menu pada data transaksi penjualan RH Store. Metode yang digunakan adalah pendekatan kuantitatif deskriptif dengan mengikuti tahapan Knowledge Discovery in Databases (KDD), meliputi seleksi data, pembersihan data, transformasi ke bentuk market basket, serta pembentukan aturan asosiasi. Data yang digunakan berupa 101 transaksi penjualan pada periode Juli hingga September 2025 dan dianalisis menggunakan aplikasi Orange Data Mining. Pengujian dilakukan dengan beberapa kombinasi nilai support dan confidence, yaitu 30%-60%, 40%-80%, dan 50%-90%. Hasil penelitian menunjukkan bahwa pada nilai support 50% dan confidence 90% diperoleh 17 aturan asosiasi dengan nilai confidence tertinggi sebesar 95% dan seluruh nilai lift lebih besar dari 1. Produk Roti Tumpuk, Roti Bulat, dan Roti Kukus memiliki tingkat keterkaitan paling dan sering muncul sebagai consequent. Hasil analisis ini dapat dimanfaatkan sebagai dasar dalam penyusunan menu paket, strategi promosi, serta pengelolaan persediaan produk di RH Store.   Sales fluctuations experienced by RH Store indicate the need to optimize the use of transaction data to support business decision-making. To date, sales transaction data have not been fully utilized to identify menu selection patterns at RH Store. This study employs a descriptive quantitative approach following the Knowledge Discovery in Databases (KDD) stages, including data selection, data cleaning, transformation into a market basket format, and association rule generation. The dataset consists of 101 sales transactions collected from July to September 2025 and was analyzed using Orange Data Mining. Experiments were conducted using several combinations of support and confidence thresholds, namely 30%–60%, 40%–80%, and 50%–90%. The results show that at a support threshold of 50% and a confidence threshold of 90%, 17 association rules were generated, with the highest confidence value reaching 95% and all lift values exceeding 1. The products Roti Tumpuk, Roti Bulat, and Roti Kukus exhibit the strongest associations and frequently appear as consequents. These findings can be utilized as a basis for designing menu packages, promotional strategies, and inventory management at RH Store.
PENERAPAN ALGORITMA RANDOM FOREST REGRESSION DALAM PREDIKSI HARGA SAHAM BBRI: IMPLEMENTATION OF THE RANDOM FOREST REGRESSION ALGORITHM FOR PREDICTING BBRI STOCK PRICES Winardi, Kevin; Nugroho, Yulianus Febry Tri; Johannes; Herdiatmoko, Hendrik Fery
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 17 No. 1 (2026): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol17no1.p9-18

Abstract

Pergerakan harga saham dipengaruhi oleh berbagai faktor dan bersifat fluktuatif, sehingga diperlukan metode prediksi yang mampu menangkap pola data yang kompleks. Penelitian ini bertujuan untuk memprediksi harga saham menggunakan metode Random Forest Regression. Data yang digunakan dibagi menjadi data pelatihan dan data pengujian untuk mengevaluasi kinerja model. Kinerja model dievaluasi menggunakan beberapa metrik, yaitu Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan koefisien determinasi (R²). Hasil penelitian menunjukkan bahwa model Random Forest Regression setelah optimasi menghasilkan nilai MAE sebesar 64,02,  RMSE sebesar 84,51, dan R² sebesar 0,8484. Nilai-nilai tersebut mengindikasikan bahwa model memiliki tingkat kesalahan prediksi yang rendah dan mampu menjelaskan 84,84% variasi pada data harga saham. Berdasarkan hasil tersebut, dapat disimpulkan bahwa Random Forest memiliki kinerja yang baik dan cukup andal dalam memprediksi harga saham.   Stock price movements are influenced by various factors and exhibit high volatility, making accurate prediction a challenging task. This study aims to predict stock prices using the Random Forest Regression method. The dataset is divided into training and testing sets to evaluate the model’s performance. The performance of the model is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results show that the Random Forest Regression model after optimization achieves an MAE of 64,02, an RMSE of 84,51, and an R² value of 0.8484. These results indicate a low prediction error and demonstrate that the model is able to explain 84.84% of the variance in stock price data. Therefore, it can be concluded that Random Forest is an effective and reliable method for stock price prediction.
IMPLEMENTASI SISTEM PENDUKUNG KEPUTUSAN MENGGUNAKAN METODE ADDITIVE RATIO ASSESSMENT (ARAS) UNTUK PENENTUAN SMK TERBAIK DI KOTA KUPANG: IMPLEMENTATION OF A DECISION SUPPORT SYSTEM USING THE ADDITIVE RATIO ASSESSMENT (ARAS) METHOD TO DETERMINE THE BEST VOCATIONAL HIGH SCHOOL (SMK) IN KUPANG CITY Tote, Gabriel Patrisius; Bulan, Semlinda Juszandri
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 17 No. 1 (2026): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol17no1.p1-8

Abstract

Penentuan Sekolah Menengah kejuruan (SMK) terbaik di kota Kupang adalah suatu keputusan yang paling penting bagi para siswa, orang tua, dan pemangku kepentingan pendidikan. Dalam konteks ini, Sistem Pendukung Keputusan (SPK) dapat menjadi alat yang efektif untuk membantu proses pengambilan keputusan tersebut. Salah satu metode SPK dapat digunakan adalah metode Additive Ratio Assessment (ARAS). Penelitian ini bertujuan untuk mengimplementasikan metode ARAS dalam pengambilan keputusan penentuan SMK terbaik di Kota Kupang. Metode ARAS digunakan untuk menilai dan membandingkan kinerja dan beberapa alternatif SMK berdasarkan berbagai kriteria yang relevan. Kriteria – kriteria tersebut dapat mencakup akreditasi, jumlah siswa, SPP, dan jumlah guru. Data mengenai kriteria-kriteria tersebut dikumpulkan dan diolah untuk menghasilkan nilai-nilai penilaian kinerja relatif dari masing-masing alternatif SMK. Kemudian, metode Additive Ratio Assessment (ARAS) digunakan untuk menghitung skor agregat dan merangking alternatif-alternatif tersebut. Hasil dari penelitian ini adalah website yang mampu menyajikan informasi yang komprehensif dan terstruktur mengenai berbagai faktor yang relevan dalam pemilihan sekolah menengah kejuruan terbaik yang dapat membantu para siswa, orang tua, dan pemangku kepentingan Pendidikan dalam memilih SMK terbaik berdasarkan preferensi terhadap kriteria-kriteria yang ada.   Determining the best vocational high school (SMK) in the city of Kupang is the most important decision for students, parents and education stakeholders. In this context, Decision Support Systems (DSS) can be an effective tool to assist the decision-making process. One SPK method that can be used is the Additive Ratio Assessment (ARAS) method. This research aims to implement the ARAS method in making decisions to determine the best vocational school in Kupang City. The ARAS method is used to assess and compare the performance of several vocational school alternatives based on various relevant criteria. These criteria can include accreditation, number of students, tuition fees, and number of teachers. Data regarding these criteria is collected and processed to produce relative performance assessment values for each alternative vocational school. Then, the Additive Ratio Assessment (ARAS) method is used to calculate the aggregate score and rank the alternatives. The result of this research is a website that is able to present comprehensive and structured information regarding various factors that are relevant in selecting the best vocational high school which can help students, parents and education stakeholders in choosing the best vocational school based on preferences for existing criteria.
ANALISIS KUALITAS PERANGKAT LUNAK PADA WEBSITE SARAH DI UNIVERSITAS SARI MULIA: SOFTWARE QUALITY ANALYSIS OF THE SARAH WEBSITE AT UNIVERSITAS SARI MULIA Syarifuddin; Annisa, Nor
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 17 No. 1 (2026): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol17no1.p112-120

Abstract

Sistem Aplikasi Penerimaan Mahasiswa Baru (SARAH) merupakan platform strategis Universitas Sari Mulia dalam mendukung proses rekrutmen mahasiswa secara daring. Kualitas perangkat lunak sistem ini secara langsung memengaruhi efisiensi proses bisnis, pengalaman pengguna, dan citra institusi. Penelitian ini bertujuan untuk mengevaluasi kualitas perangkat lunak website SARAH berdasarkan standar ISO/IEC 25010 dengan fokus pada dimensi Functional Suitability, Performance Efficiency, dan Usability. Metode yang digunakan meliputi pengujian black-box untuk validasi fungsional, pengukuran kinerja frontend menggunakan Core Web Vitals melalui Google Lighthouse, serta pengujian skalabilitas backend menggunakan stress testing berbasis Apache JMeter. Hasil penelitian menunjukkan adanya cacat fungsional kritis berupa tautan mati pada navigasi utama, kinerja mobile yang buruk dengan nilai Largest Contentful Paint (7,9 detik), serta degradasi performa server pada beban tinggi dengan waktu respons mencapai 22,3 detik. Temuan ini mengindikasikan bahwa kualitas perangkat lunak SARAH masih berada pada tingkat risiko tinggi. Penelitian ini berkontribusi dengan menyajikan model evaluasi kualitas sistem PMB yang komprehensif dan memberikan rekomendasi teknis berbasis data untuk peningkatan keandalan sistem.   The New Student Admission Application System (SARAH) is a strategic platform for Sari Mulia University in supporting the online student recruitment process. The quality of this system's software directly affects business process efficiency, user experience, and institutional image. This study aims to evaluate the quality of the SARAH website software based on the ISO/IEC 25010 standard with a focus on the dimensions of Functional Suitability, Performance Efficiency, and Usability. The methods used include black-box testing for functional validation, front-end performance measurement using Core Web Vitals through Google Lighthouse, and back-end scalability testing using Apache JMeter-based stress testing. The results show critical functional defects in the form of dead links in the main navigation, poor mobile performance with a Largest Contentful Paint value of 7.9 seconds, and server performance degradation under high load with a response time reaching 22.3 seconds. These findings indicate that the quality of the SARAH software remains at a high risk level. This study contributes by presenting a comprehensive PMB system quality evaluation model and providing data-driven technical recommendations for improving system reliability
EVALUASI EFEKTIVITAS TEKNIK PRIVACY-PRESERVING: K-ANONYMITY, L-DIVERSITY, T-CLOSENESS PADA DATA SENSITIF: EVALUATION OF THE EFFECTIVENESS OF PRIVACY-PRESERVING TECHNIQUES—K-ANONYMITY, L-DIVERSITY, AND T-CLOSENESS—ON SENSITIVE DATA Ronal; Tambunan, Desy Ebigael; Yuliana
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 17 No. 1 (2026): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol17no1.p102-111

Abstract

Perlindungan privasi menjadi aspek krusial dalam pengumpulan, pengolahan, dan publikasi data sensitif, namun potensi risiko kebocoran informasi dapat menimbulkan konsekuensi hukum maupun kerugian reputasi. Untuk menjaga keseimbangan antara kegunaan data dan privasi individu, teknik anonimisasi menjadi pendekatan utama, termasuk penerapan k-anonymity dan evaluasi menggunakan l-diversity dan t-closeness. Penelitian ini bertujuan untuk mengevaluasi efektivitas teknik-teknik tersebut dalam mengurangi risiko pengungkapan identitas dan atribut sensitif pada dataset kesehatan. Studi kasus menggunakan 55500 dataset medis dengan quasi-identifier Age, Gender, dan Blood Type, serta atribut sensitif Medical Condition. Dataset dianonimkan menggunakan k-anonymity melalui proses generalisasi dan supresi untuk membentuk equivalence class dengan ukuran minimum k ? 5. Selanjutnya, dataset dievaluasi menggunakan l-diversity untuk mengukur keberagaman atribut sensitif dalam setiap kelompok, serta t-closeness untuk menilai kesamaan distribusi atribut sensitif terhadap distribusi global menggunakan Earth Mover’s Distance (EMD). Hasil pengujian menunjukkan bahwa seluruh equivalence class telah memenuhi k ? 5 dengan suppression rate sebesar 1,15%. Evaluasi l-diversity menunjukkan tidak terdapat equivalence class dengan l < 2, sehingga risiko attribute disclosure dapat diminimalkan. Pengujian t-closeness menggunakan Earth Mover’s Distance (EMD) menunjukkan mayoritas kelas memiliki EMD ? 0,15 dan hanya satu kelas dengan nilai sedikit di atas ambang batas t = 0,2. Dari sisi utilitas data, nilai Normalized Generalized Information Loss (NGIL) sebesar 0,079 (7,9%) dan AECS sebesar 6,28 menunjukkan tingkat kehilangan informasi yang rendah tanpa terjadi over-generalization. Secara keseluruhan, kombinasi metode yang diterapkan berhasil mencapai keseimbangan antara perlindungan privasi dan data utility.   Privacy protection has become a crucial aspect in the collection, processing, and publication of sensitive data, as potential risks of information leakage may lead to legal consequences and reputational damage. To maintain a balance between data utility and individual privacy, anonymization techniques serve as a primary approach, including the implementation of k-anonymity and its evaluation using l-diversity and t-closeness. This study aims to evaluate the effectiveness of these techniques in reducing the risk of identity and attribute disclosure in a healthcare dataset. The case study utilizes a 55500 dataset medis containing the quasi-identifiers Age, Gender, and Blood Type, as well as the sensitive attribute Medical Condition. The dataset was anonymized using k-anonymity through generalization and suppression to form equivalence classes with a minimum size of k ? 5. Subsequently, the dataset was evaluated using l-diversity to measure the diversity of sensitive attributes within each group, and t-closeness to assess the similarity between the distribution of sensitive attributes in each group and the global distribution using Earth Mover’s Distance (EMD). The results indicate that all equivalence classes satisfy k ? 5 with a suppression rate of 1.15%. The l-diversity evaluation shows that no equivalence class has l < 2, thereby minimizing the risk of attribute disclosure. The t-closeness assessment reveals that the majority of classes have EMD ? 0.15, with only one class slightly exceeding the threshold of t = 0.2. In terms of data utility, the Normalized Generalized Information Loss (NGIL) value of 0.079 (7.9%) and an AECS of 6.28 indicate a low level of information loss without over-generalization. Overall, the combination of methods successfully achieves a balance between privacy protection and data utility, ensuring that the dataset remains suitable for further analysis and secondary data publication.

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