Article Per Year (5 Year)
p-Index From 2021 - 2026
0.702
P-Index
This Author published in this journals
All Journal DIGINTEL-AI : DIGital INnovation and inTELligence – AI
Irwansyah
Teknik Informatika, Fakultas Teknologi Industri dan Informatika, Universitas Muhammadiyah Prof. Dr. HAMKA, Jakarta

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
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.
Implementasi Algoritma K-NN Pada Sosial Media X Untuk Analisis Sentimen Pengalaman Warganet Tinggal Di Luar Negeri Salsa Billa Permana Putri; Irwansyah; Tupan Tri M
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.v1i1.4

Abstract

The development of information technology, especially through social media such as Twitter, has changed the way people search for information. With more than 6.43 million users in Indonesia, Twitter has become the main platform for sharing opinions. This study aims to analyze the sentiments of Indonesian citizens (WNI) living abroad, who often face challenges and opportunities in adapting to new environments. Given the increasing number of WNI, reaching over 9 million in 2020, understanding their sentiments is crucial. The K-Nearest Neighbor (KNN) method was used to classify sentiments as positive, negative, or neutral. This study involved data collection through the tweet-harvest technique, where 1,060 comments were successfully collected, and 600 of them met the relevance criteria for analysis. The analysis results showed that 60.4% of sentiments were neutral, 34.1% were positive, and 5.5% were negative, with the KNN model achieving an accuracy of 81.67%. Model evaluation revealed the highest precision in the neutral class and a recall of 1.00, although the positive and negative classes require further optimization. This study is expected to provide insights for the public and decision-makers regarding the experiences of Indonesian citizens abroad.
Analisis Sentimen Terhadap Komentar Video IShowSpeed Tour Indonesia Pada YouTube Menggunakan Metode SVM Daffa Ihza Kurniawan; Irwansyah; Ahmad Taufik
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.v1i1.5

Abstract

In September 2024, international influencer IShowSpeed's visit to Indonesia attracted public attention and triggered various reactions on social media, especially YouTube. The content in the form of tours and live broadcasts conducted by IShowSpeed generated various comments from users, ranging from positive, negative, to neutral sentiments. This study aims to analyze these sentiments using the Support Vector Machine (SVM) method with a linear kernel. A total of 43,778 comments were used in this study. The classification results showed an accuracy of 91.1%. For negative sentiment, precision 86%, recall 78%, and f1-score 82% were obtained. Neutral sentiment achieved 90% precision, 94% recall, and 92% f1-score. Meanwhile, positive sentiment obtained a precision, recall, and f1-score of 94% each. These findings show that the majority of user comments are positive, indicating that IShowSpeed and its content are well received by Indonesian audiences.
Co-Authors Ahmad Taufik Ari Rama Novryadi Daffa Ihza Kurniawan Moh Shidqon Muhammad Aushofi Salsa Billa Permana Putri Tupan Tri M