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All Journal DIGINTEL-AI : DIGital INnovation and inTELligence – AI
Moh Shidqon
Fakultas Ekonomi dan Bisnis, Universitas Trisakti, Jakarta

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Implementasi Data Mining Untuk Menganalisis Pola Penimbangan Sampah Menggunakan Algoritma Apriori Muhammad Aushofi; Irwansyah; Moh Shidqon
DIGINTEL-AI : DIGital INnovation and inTELligence – AI Vol. 1 No. 1 (2025): October
Publisher : PT Ajira Karya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66217/digintel-ai.v1i1.1

Abstract

Pandan Wangi Waste Bank's weighing transaction data has not been maximized and is not used for further purposes. Pandan Wangi Waste Bank receives 42 types of waste from the community, but has no information about the weighing pattern of the waste deposited by the community. Therefore, managers sometimes have difficulty in planning better storage and management. This study aims to analyze waste weighing patterns based on weighing transaction data to identify customer weighing behavior, find the types of waste that are often weighed together, and determine the support, confidence, and lift ratio values of each association rule generated. The technique used is a quantitative method and to process the weighing transaction data into information using the apriori data mining algorithm.  From 866 weighing data for two years from May 2022 to March 2024, this research produces four rules that have a good lift ratio value with a minimum support value of 0.1 and a minimum confidence of 0.8. The most frequently weighed type of waste is the mixed bucket type with a support value of 69.9%. Then for the type of waste that is most often weighed simultaneously is if weighing boncos, and clean mineral bottles, then also weighing mixed buckets with a support value of 0.11 and confidence of 0.87.
Analisis Sentimen Masyarakat Terhadap Kinerja Presiden Indonesia Joko Widodo Periode Kedua Menggunakan Metode Naïve Bayes dan SVM Ari Rama Novryadi; Irwansyah; Moh Shidqon
DIGINTEL-AI : DIGital INnovation and inTELligence – AI Vol. 1 No. 1 (2025): October
Publisher : PT Ajira Karya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66217/digintel-ai.v1i1.2

Abstract

The advancement of information technology, particularly social media, has transformed the way the public expresses opinions on public issues, including the performance of the president. This study aims to analyze public sentiment regarding the performance of the Indonesian President during his second term using two text classification methods: Naïve Bayes and Support Vector Machine (SVM). The dataset consists of 1,003 tweets collected from social media platform X between September 2023 and September 2024. Prior to classification, the data underwent preprocessing steps such as cleaning, normalization, case folding, stopword removal, and stemming. The classification results revealed that 57.83% of tweets expressed negative sentiment, 34.40% positive, and 7.78% neutral. Negative sentiments were predominantly associated with issues such as price hikes, controversial policies, and allegations of corruption, whereas positive sentiments related mainly to infrastructure development and social assistance programs. Performance evaluation indicated that the SVM method achieved a higher accuracy of 71.6%, outperforming Naïve Bayes, which achieved 65.2% accuracy. This study concludes that social media serves as an effective data source for capturing broad public opinion, and that SVM is a more effective classifier than Naïve Bayes for sentiment analysis of social media text data.
Co-Authors Ari Rama Novryadi Irwansyah Muhammad Aushofi