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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Matrik METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Sebatik Jurnal Nasional Komputasi dan Teknologi Informasi Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Building of Informatics, Technology and Science Just TI (Jurnal Sains Terapan Teknologi Informasi) TEPIAN Journal of Social Responsibility Projects by Higher Education Forum Informatika Wicida Digital Transformation Technology (Digitech)
Harpad, Bartolomius
STMIK Widya Cipta Dharma
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Neuroscience Social Sciences Transportation

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Journal : building of informatics technology and science

 1 
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 Ahmad Fahrijal Pukeng Ahmad Rofiq Hakim Aldy Rivaldo Aldy Septian Derry Amelia Yusnita Amelia Yusnita Ammar Ibnu Arif Azahari Christianus Pakembongan Dicky Rifaldi Ekawati, Hanifah Evander Yose Gultom Fadillah Candrika Pratama Helda Priscila Ita Arfyanti Ita Arfyanti Laramitha Maulana Bakti Meilinda Wulandari Muhamad Almazazi Metekohy Muhammad Ibnu Sa’ad Muhammad Sirajul Amin Mubarak Nizar Kharismi Nurhuda, Asep Pahrudin, Pajar Pitrasacha Adytia Ramadhani Suciansyah Ramadhani Suciansyah Reza Andrea Rizki Galang Rahmadani Rizky Meizal Saputra Romanus Kristian Salmon Salmon Sony Darmawan Sunarto Syafriadi Meidianta Syamsuddin Mallala Uleh Lod Yohanes Rombe Paran Yunita Yunita