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All Journal Katalogis Jurnal Penelitian Komunikasi dan Opini Publik JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Matrik Sebatik Jurnal Nasional Komputasi dan Teknologi Informasi Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Journal of Technopreneurship and Information System (JTIS) Building of Informatics, Technology and Science TEPIAN Buletin Poltanesa Bulletin of Computer Science Research Bulletin of Information Technology (BIT) DiJITAC : Digital Journal of Information Technology and Communication Informatika Wicida Research in Education, Technology, and Multiculture
Salmon Salmon
STMIK Widya Cipta Dharma
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Journal : building of informatics technology and science

 1 
Penerapan Metode Simple Additive Weighting (SAW) dan SWARA dalam Pendukung Keputusan Pemilihan Penerimaan Karyawan Apoteker Salmon, Salmon; Arfyanti, Ita
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (454.746 KB) | DOI: 10.47065/bits.v4i1.1488

Abstract

Drugs are the most important needs in alleviating and overcoming complaints of illness suffered by patients, a patient will certainly go to the nearest drug store or pharmacy to ask for advice in drug selection to a pharmacist, as a pharmacy or drug store Of course, it is very dangerous to choose a pharmacist without proper knowledge and insight into medicines. Failure to choose a pharmacist will result in losses to the company or pharmacy as well as patient safety, so an expert understanding is needed to determine the standards of a pharmacist to be trusted, selecting employees using a decision support system is the right step to reduce the risk that will occur. in the future. The decision support system in this study uses the SWARA method in determining the weight of the criteria based on expert opinion and the Simple Additive weigh (SAW) method as a ranking method based on the highest value. The results of this study selected alternative A4 on behalf of Tika which has a value of 95% as the alternative that most meets the standards
Prediksi Persediaan Bahan Baku Makanan Menerapkan Algoritma Apriori Data Mining Salmon, Salmon; Azahari, Azahari; Yusnita, Amelia
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2563

Abstract

The company's operational activities are inseparable from the supply of raw materials that must be met every day to meet consumer demand. The restaurant uses raw materials, namely vegetables, raw meat which includes beef and chicken, yellow noodles and soun noodles, and the main seasoning. Sales of food at this restaurant quite a lot in a day. This will produce sales data that will continue to grow every day, but this data is useless if it is not processed again to get the knowledge contained in the data. The Apriori algorithm is a method for finding patterns of relationships between one or more items from a dataset. Thus the pile of data that has been collected can produce a sales pattern, from which the customer's buying interest in food can be identified. From the results of research using a data sample of 18 items with a minimum of 20% Support and 50% Confidence, it produces 5 interesting rules with the highest Support reaching 33.33% and the highest Confidence reaching 100%.
Investment Decision-Making for High-Potential Startups in the Digital Economy Using AHP and VIKOR Salmon Salmon; Rizki Galang Rahmadani; Bartolomius Harpad
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8633

Abstract

The rapid growth of the digital economy has driven the emergence of numerous startup companies that play a vital role as catalysts for innovation and business transformation in the modern era. However, the increasing number of startups poses a major challenge for investors in selecting the most potential and profitable investment opportunities. The main problem lies in the multi-criteria evaluation process, which involves various aspects such as market potential, product innovation, business model, team performance, and financial stability. To address this complexity, this study applies a combination of the Analytical Hierarchy Process (AHP) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods as an objective and measurable multi-criteria decision-making approach. The AHP method is utilized to determine the priority weights of each criterion through a pairwise comparison process. The results show that market potential (C1) is the most dominant criterion with a weight of 0.458, followed by product innovation (C2) with a weight of 0.247, and business model (C3) with a weight of 0.144. Meanwhile, team performance (C4) and financial stability (C5) have relatively lower weights of 0.105 and 0.046, respectively. These findings indicate that market and innovation aspects are the primary factors influencing startup investment feasibility. Furthermore, the VIKOR method is employed to rank the alternatives based on compromise solutions toward the ideal outcome. The results reveal that startup A17 has the lowest compromise value (Q = 0.0000), making it the most optimal investment alternative, followed by A4 (Q = 0.0303) and A19 (Q = 0.0586). This study demonstrates that the combination of AHP and VIKOR methods provides a comprehensive, objective, and consistent analysis in the decision-making process for digital startup investments. The proposed approach assists investors in evaluating startups more systematically and accurately based on the priority of relevant criteria in the context of the dynamic digital economy. Therefore, a decision support system based on the AHP-VIKOR method can serve as an effective solution for decision-makers to identify and select the most promising startups for future development.
Customer Sentiment Analysis of E-Commerce Products Using the Naïve Bayes Method and Word Embedding Bartolomius Harpad; Azahari Azahari; Salmon Salmon
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8879

Abstract

This study discusses customer sentiment analysis toward e-commerce products using the Naïve Bayes method combined with Word Embedding techniques to enhance the semantic understanding of Indonesian-language customer reviews. The research background is based on the rapid growth of e-commerce, which has created a strong need to understand consumer opinions through online reviews. The main challenge in sentiment analysis lies in the complexity of natural language, such as the use of informal words, abbreviations, and diverse emotional expressions. This study utilizes 40,607 Tokopedia customer reviews across five product categories with three sentiment labels (positive, neutral, and negative). The research stages include data collection, text preprocessing (case folding, tokenization, stopword removal, stemming, and slang normalization), feature representation using Word2Vec and FastText, and classification using Multinomial Naïve Bayes. Experimental results show that the combination of Word2Vec and Naïve Bayes achieved an accuracy of 87.92%, while FastText and Naïve Bayes improved accuracy to 91.52%. The FastText-based model proved superior in handling morphological variations and non-standard spellings, making it more effective for Indonesian customer review texts. The WordCloud visualization reveals the dominance of positive words such as “sesuai” (appropriate), “barang” (item), and “cepat” (fast), indicating customer satisfaction regarding product conformity and service speed. The Confusion Matrix results indicate a bias toward the positive class due to data imbalance, where the model still struggles to recognize neutral and negative classes. Overall, this study demonstrates that integrating Word Embedding with Naïve Bayes enhances classification performance and provides richer semantic representations compared to traditional Bag of Words approaches. This approach has the potential to be applied in developing data-driven recommendation systems and marketing strategies within Indonesia’s e-commerce ecosystem.
Co-Authors Abil Firnanda Achmad Sadzali Muftisjar Ahmad Rofiq Hakim Alfan Oktaviansyah Maulana Putra Amelia Yusnita Andi Yusika Andi Yusika Rangan Andi Yusika Rangan Andriawan Angel Crystia Nadia Arsita Aulia Abdul Rahman Awang H. Kridalaksana Azahari Azahari, Azahari Bagus Ari Ramadhan Bartolomius Harpad Christianus Pakembongan Dewi Rosita Fachry Abda El Rahman Haeruddin Haeruddin Harianto, Kusno Harpad, Bartolomius Heny Pratiwi Ignasius Aditya Anggoro Putra Imran Imran Ita Arfyanti Ivan Haristyawan Kusnandar Kusnandar Kusnandar Kusnandar Kusnandar Kusnandar Mardianus Maulita Maulita Mini H Muhamad Risky Ramadhani Muhammad Beny Hafis Muhammad Fahmi Muhammad Jumal Sandika Muhammad Rezha Nur Hakiki Nadhif Nandana Kartomo Nur Haeda Nurhasanah Nurhasanah Nurhuda, Asep Nurlaili Nurlaili Nursobah, Nursobah Pitrasacha Adytia Puji Astuti Amalia Rachmad Fauzi Rajiansyah Reza Andrea Rizki Galang Rahmadani Rizky Meizal Saputra Rizky Zakariyya Rasyad Roffi Maulidi Ronny Sadya Winda Sari Siti Lailiyah Siti Lailiyah Sri Rakhmawati Sugeng Suryani Sugih Niansyah Suryani, Sugeng Tino Purniawan Widyatmike Gede Mulawarman Yohanes Rombe Paran Yunita Yunita