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All Journal Jurnal Informasi dan Teknologi Bulletin of Computer Science Research
Okfalisa
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Marketing Strategy UMKM Dengan CRISP-DM Clustering & Promotion Mix Menggunakan Metode K-Medoids Iswavigra, Dwi Utari; Endriani Zen, Lova; Okfalisa; Hanim, Hafizah
Jurnal Informasi dan Teknologi 2023, Vol. 5, No. 1
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v5i1.260

Abstract

The development of MSMEs is something that has a very large influence on the economy in Indonesia. The local MSME market is even able to compete globally as shown by the large number of incoming requests from abroad. Even so, MSMEs in Indonesia have experienced fluctuations due to the economic crisis. The fluctuations that occurred showed a decrease in the number of MSMEs by 0.003%. The Executive Director of the Institute For Economics (Indef) said that the fluctuations that occurred in MSMEs resulted in unstable economic development in the second quarter. From this problem, the CRISP-DM Clustering process was carried out to process existing MSME data to determine the right promotion strategy in developing MSMEs based on the type of business undertaken, turnover and assets owned. This research was conducted using the K-Medoids algorithm by forming 3 clusters for data processing. The data processed were 71 MSME data throughout Solo Raya, where in cluster 1 there were 25 MSMEs which were dominated by types of businesses in the fashion sector with an average turnover and assets between IDR 1,000,000 - IDR 5,000,000. Cluster 2 consists of 39 MSMEs which are dominated by types of businesses in the culinary field with average turnover and assets between IDR 1,000,000 - IDR 5,000,000 and in cluster 3 as many as 7 MSMEs which are dominated by types of businesses in other fields ( excluding 6 other types of business) with an average asset of ≥ Rp. 30,000,000 and a turnover between Rp. 21,000,000 - Rp. 25,000,000.
Klasifikasi Sentimen Masyarakat Terhadap Revisi Undang-Undang Tentara Nasional Indonesia Menggunakan Naïve Bayes Classifier Abdul Haris Kurnia Sandi Harahap; Haerani, Elin; Oktavia, Lola; Okfalisa; Kurnia, Fitrah
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.615

Abstract

The revision of the Indonesian National Armed Forces Bill (RUU TNI) has become a hot topic in Indonesian public policy and has sparked controversy among the public due to its sudden emergence and lack of open planning process. This has raised concerns about the potential for military domination and the return of the dual function of the ABRI (Indonesian Armed Forces). The classification of public sentiment towards the RUU TNI is the focus of this study. Comments are categorized into two types of sentiment classes, namely positive and negative. The research stages include data collection, sentiment labeling, data cleaning, text normalization to lowercase letters, sentence or document segmentation into smaller parts, text data normalization, negation handling, stopword removal, and stemming, weighting using the TF-IDF technique, model classification development, and evaluation of the model's performance. The Naïve Bayes Classifier method classified 1,547 comment data points collected from two Instagram social media accounts. The Naïve Bayes Classifier model achieved an accuracy of 83.74%, precision of 81.17%, recall of 87.86%, and an F1-score of 84.38%. This study has limitations, including the limited amount of data collected. These include an imbalance in the amount of data between sentiment categories, data from only one social media platform, and the suboptimal identification of positive and negative sentiments. It is recommended that future research compare this method with other classification methods, expand the dataset, broaden the scope of data collection by involving various social media platforms over a wider time span, thereby providing a more comprehensive picture of public opinion, and test a wider range of algorithm combinations. This study can serve as an initial indicator for rapid policy evaluation, where positive or negative comments from the public on social media can provide important input in assessing the effectiveness of a policy.
Co-Authors Abdul Haris Kurnia Sandi Harahap Elin Haerani Endriani Zen, Lova Hanim, Hafizah Iswavigra, Dwi Utari Kurnia, Fitrah Oktavia, Lola