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All Journal Sistemasi: Jurnal Sistem Informasi PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science Jurnal Informatics Nivedita
Zarry, Cindy Kirana
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Classifications of Offline Shopping Trends and Patterns with Machine Learning Algorithms Muta'alimah, Muta'alimah; Zarry, Cindy Kirana; Kurniawan, Atha; Hasysya, Hauriya; Firas, Muhammad Farhan; Nadhirah, Nurin
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 1: PREDATECS July 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i1.1099

Abstract

Advancements in technology have made online shopping popular among many. However, the use of offline marketing models is still considered a profitable and important way of business development. This can be seen in the 2022 Association of Retail Entrepreneurs of Indonesia (APRINDO), which states that  60% of Indonesians shop offline, and in 2023, more than 75% of continental European consumers will prefer to shop offline. This is because many benefits can be achieved through offline marketing that cannot be obtained from online marketing. Therefore, classification of patterns and trends is performed to compare the results of the algorithms under study. Furthermore, this research was conducted to help offline retailers understand consumption patterns and trends that affect purchases. The algorithms analyzed in this study are K-Nearest Neighbor (K-NN), Naive Bayes, and Artificial Neural Network (ANN). As a result, the ANN algorithm obtained the highest confusion matrix results with an Accuracy value of 96.38%, Precision of 100.00%, and Recall of 100.00%. Meanwhile, when the Naive Bayes algorithm was used, the lowest Accuracy value was 57.39%, the Precision value was 57.86%, and when the K-NN algorithm was used, the Recall value was as low as 92.00%. These results indicate that the ANN algorithm is better at classifying offline shopping image data than the K-NN and Naive Bayes algorithms
An Analysis of the Academic Information System Quality at Universitas Lancang Kuning (Smart Unilak) using the WebQual 4.0 and McCall Methods Zarry, Cindy Kirana; Megawati, Megawati; Rahmawita, Medyantiwi; Salisah, Febi Nur
SISTEMASI Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5164

Abstract

Websites have become an essential medium for information dissemination and marketing, particularly in the context of higher education. Universitas Lancang Kuning has implemented an Academic Information System (Smart Unilak) to improve the efficiency of academic services. However, observations and interviews with the PDDIKTI administrator revealed several issues, including a lack of updated information, difficulties in completing the Study Plan Card (KRS), and concerns regarding data security. To evaluate the system's quality, an analysis was conducted using the WebQual 4.0 and McCall methods. The WebQual analysis showed that the average respondent scores ranged from 3 to 4, which were interpreted as “Satisfactory” to “Very Satisfactory.” Additionally, the McCall method yielded an overall quality score of 89.28%, placing the system in the “Excellent” category. While respondents expressed satisfaction with the information and security provided by the system, there remains room for improvement in terms of communication ease. The findings of this study serve as an evaluation reference for website developers and a basis for future research. It is recommended that subsequent studies incorporate direct usability testing with users to identify issues that may not surface through surveys and questionnaires. Observing users as they interact with the system can provide valuable insights for further enhancement.
Analisis Dampak Risiko IT Pada Website Sistem Informasi Pelayanan Administrasi Surat Menyurat (SIASY) Menggunakan Metode FMEA Zarry, Cindy Kirana; Tshamaroh, Muthia; Agesti, Suci; Megawati
Journal Informatics Nivedita Vol 1 No 1 (2024): Journal Informatics Nivedita
Publisher : Universitas Hindu Negeri I Gusti Bagus Sugriwa Denpasar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25078/nivedita.v1i1.4390

Abstract

Sistem Informasi Pelayanan Administrasi Surat Menyurat (SIASY) di perguruan tinggi menghadapi berbagai tantangan dan risiko IT yang dapat mengganggu efisiensi layanan administrasi. Isu-isu seperti akses terbatas bagi pimpinan, ketidakakuratan data, dan masalah sinkronisasi dengan sistem keuangan menjadi fokus utama yang perlu dianalisis untuk meningkatkan keandalan sistem. Penelitian ini bertujuan untuk mengeksplorasi penerapan metode Failure Mode and Effects Analysis (FMEA) dalam mengidentifikasi dan mengelola risiko yang ada pada SIASY. Dengan demikian, diharapkan dapat memberikan rekomendasi bagi pengelola sistem untuk meningkatkan kualitas dan keandalan layanan administrasi. Metode FMEA juga berguna untuk mengidentifikasi titik-titik lemah dalam sistem, mengevaluasi risiko yang terkait, dan memberikan solusi untuk memitigasi masalah yang ada. Data yang digunakan dalam riset ini meliput informasi tentang proses administrasi yang dilakukan melalui SIASY, serta hasil wawancara dan survei dengan pengguna sistem (mahasiswa, dosen, dan pegawai) untuk mendapatkan wawasan mengenai tantangan dan kebutuhan mereka. Hasil penelitian menunjukkan bahwa penerapan FMEA dapat membantu dalam mengidentifikasi dan mengelola risiko TI yang ada pada SIASY. Dengan mengatasi masalah seperti akses terbatas, ketidakakuratan data, dan sinkronisasi sistem, diharapkan efisiensi dan kepuasan pengguna dalam proses administrasi di perguruan tinggi dapat meningkat. Penelitian ini memberikan kontribusi penting bagi pengelola sistem dalam upaya meningkatkan keandalan dan kualitas layanan administrasi
Co-Authors Agesti, Suci Febi Nur Salisah, Febi Nur Firas, Muhammad Farhan Hasysya, Hauriya Kurniawan, Atha Megawati Megawati - Muta'alimah, Muta'alimah Nadhirah, Nurin Rahmawita, Medyantiwi Tshamaroh, Muthia