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DIGINTEL-AI : DIGital INnovation and inTELligence – AI
Published by PT. Ajira Karya Indonesia
ISSN : -     EISSN : 31238076     DOI : https://doi.org/10.66217/digintel-ai.v1i2.11
Core Subject :
DIGINTEL-AI : DIGital INnovation and inTELligence – AI is a scientific journal published by PT Ajira Karya Indonesia in collaboration with the Informatics Engineering Study Program of ISTEK Widuri. This journal contains articles on research results, scientific studies, and applied studies in the field of digital innovation and artificial intelligence (Artificial Intelligence). This journal aims to be a publication platform for academics, researchers, students, and practitioners who want to disseminate their scientific findings that are relevant to the development of intelligent technology and digital transformation. The main focus of DIGINTEL-AI : DIGital INnovation and inTELligence – AI includes the development of innovative solutions, the implementation of the latest digital technology, and the study of intelligent systems in various fields of life. DIGINTEL-AI : DIGital INnovation and inTELligence – AI is published twice a year (October and April), and accepts manuscripts in both Indonesian and English. All submitted articles will go through a peer-review process to ensure the quality and originality of the manuscript.
Arjuna Subject : -
Articles 10 Documents
 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.
Analisis Pola Pembelian Konsumen Di Rumah Makan Tepi Laut Baubau Menggunakan Algoritma Apriori Fadil Firmansyah; Irwansyah; Agus Budiyantara
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.v1i1.3

Abstract

Baubau seaside restaurants have not utilized transaction data welleven though transaction data can be found behavior or consumer purchasing patterns. This study aims to analyze consumer purchasing patterns based on transaction data to identify consumer purchasing behavior, find items that are often purchased simultaneously in one transaction, and find out the value of support, confidence, and lift ratio of each association rule generated through the analysis process. Of the 701 records contained in the transaction data, the apriori algorithm method is used in analyzing consumer purchasing patterns with support parameters, association rules with confidence parameters and measuring association rules with lift ratio. Based on the results of apriori analysis, three association rules are obtained, namely: purchase of sunu ori tends to followed by mineral water (support 0.102857; confidence 0.808989; lift 2.6339), purchase of iced tea and crispy squid followed by bobara hm (support 0.087143; confidence 0.910448; lift 3.1550), and purchase of crispy squid and bobara hm followed by iced tea (support 0.087143; confidence 0.835616; lift 5.222603).
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.
Klasifikasi Metode Naïve Bayes pada Ulasan Pengguna Aplikasi Dazzcam untuk Pengeditan Foto Vintage di App Store Salsa Dwi Agistina; Irwansyah; Agus Budiyantara
DIGINTEL-AI : DIGital INnovation and inTELligence – AI Vol. 1 No. 2 (2026): April
Publisher : PT Ajira Karya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66217/digintel-ai.v1i2.9

Abstract

The rapid growth of mobile applications has increased the importance of user-generated reviews as a source of information for evaluating application quality and user satisfaction. Dazzcam, a photo editing application known for its vintage-style filters, has gained significant popularity among iOS users. This study aims to classify user reviews from the App Store into positive and negative sentiment categories using the Naïve Bayes algorithm and to evaluate the performance of the model. A total of 911 reviews were collected and divided into training and testing datasets with a ratio of 80:20. The research methodology includes data preprocessing, feature extraction using TF-IDF, and classification using Naïve Bayes, followed by evaluation with a confusion matrix. The results show that 712 reviews were classified as positive and 199 as negative, with an accuracy of 79.78%, precision of 79.89%, recall of 79.78%, and F1-score of 79.53%. These findings indicate that the Naïve Bayes algorithm demonstrates good performance and can be effectively utilized for sentiment analysis of application reviews.
Penerapan Algoritma XGBoost dalam Klasifikasi Jumlah Korban Kecelakaan Kereta Api di Indonesia Selphia Nur Azzahra; Irwansyah; Tupan Tri M
DIGINTEL-AI : DIGital INnovation and inTELligence – AI Vol. 1 No. 2 (2026): April
Publisher : PT Ajira Karya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66217/digintel-ai.v1i2.10

Abstract

This study aims to classify the number of vehicle accident casualties caused by railway accidents in Indonesia into low, medium, and high-risk categories using the XGBoost algorithm, as well as to evaluate the model performance based on accuracy, precision, and recall metrics. The employed methodology is CRISP-DM, consisting of stages such as business understanding, data understanding, data preparation, modeling, evaluation, and deployment stages. The dataset was obtained from official reports of the National Transportation Safety Committee (KNKT) and online news articles from 1991 to early 2025, resulting in 112 valid records after preprocessing, including data labeling, transformation of nominal attributes, and conversion of date data into numerical form. The classification process was carried out using RapidMiner. The results show that the XGBoost model achieved an accuracy of 88.39%, with the highest precision and recall values in the low-risk class (0.91 and 0.94) and high-risk class (0.88 and 0.87), while the performance for the medium-risk class remains relatively low (precision 0.75 and recall 0.68), indicating potential data imbalance or insufficient discriminative features. Based on these findings, it can be concluded that the XGBoost algorithm is effective in classifying railway accident risk levels; however, improvements in data quality and feature selection are still needed to achieve more optimal performance.
Perbandingan Kinerja Algoritma K-Nearest Neighbor dan Decision Tree dalam Analisis Sentimen Ulasan Aplikasi DANA pada Google Play Store Khofifah Dwi Fany; Irwansyah; Moh Shidqon
DIGINTEL-AI : DIGital INnovation and inTELligence – AI Vol. 1 No. 2 (2026): April
Publisher : PT Ajira Karya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66217/digintel-ai.v1i2.11

Abstract

The rapid growth of digital wallet applications such as DANA has raised concerns regarding the quality of services provided to users. One effective approach to evaluate service quality is through sentiment analysis of user reviews on the Google Play Store platform. However, the large volume of available review data makes manual analysis inefficient. This study aims to identify the most optimal classification algorithm for sentiment analysis of DANA application reviews by comparing the performance of the K-Nearest Neighbor (K-NN) and Decision Tree algorithms. The dataset consists of 723 reviews obtained from Kaggle, divided into 578 training data and 145 testing data. The reviews are classified into three sentiment categories: positive, negative, and neutral. The research process includes data collection, filtering, preprocessing (case folding, tokenizing, stopword removal, and token length filtering), TF-IDF weighting, implementation of classification algorithms, and evaluation using a Confusion Matrix. The results show that the K-NN algorithm achieves an accuracy of 53.10%, precision of 90.32%, and recall of 41.79%, while the Decision Tree algorithm yields a higher recall but lower accuracy and precision. Based on the comparison of these evaluation metrics, the K-NN algorithm is recommended as the more optimal method, as it provides a better balance between prediction accuracy and error rate compared to the Decision Tree.
Implementasi Algoritma Long Short-Term Memory untuk Memprediksi Harga Mata Uang Kripto Litecoin Muhamad Jafar Rahadian; Irwansyah; Agus Indra P
DIGINTEL-AI : DIGital INnovation and inTELligence – AI Vol. 1 No. 2 (2026): April
Publisher : PT Ajira Karya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66217/digintel-ai.v1i2.12

Abstract

This study aims to evaluate the effectiveness of the Long Short-Term Memory (LSTM) algorithm in predicting the price of the Litecoin cryptocurrency. The dataset used consists of historical Litecoin price data against USD obtained from Yahoo Finance. Considering the high volatility of the cryptocurrency market, accurate price prediction is essential to assist investors in minimizing risks and maximizing potential returns. The LSTM method was selected due to its capability to model time-series data and capture long-term dependencies. The results show that the LSTM model is able to generate accurate predictions, achieving a Root Mean Square Error (RMSE) of 3.72% and a coefficient of determination (R²) of 91.38%. These findings indicate that the LSTM algorithm has strong potential for cryptocurrency price prediction, particularly for Litecoin.
Perbandingan Naive Bayes Classifier dan SVM untuk Analisis Sentimen Desain Seragam Atlet Indonesia pada Media Sosial X di Olimpiade Paris 2024 Azizah Salma Nida; Irwansyah; Ade Davy Wiranata
DIGINTEL-AI : DIGital INnovation and inTELligence – AI Vol. 1 No. 2 (2026): April
Publisher : PT Ajira Karya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66217/digintel-ai.v1i2.13

Abstract

The Olympics is an international sporting event held every four years and serves as a platform for countries to showcase their athletic capabilities and national identity. One aspect that attracts public attention is the design of athletes' uniforms, which not only have aesthetic value but also support athletic performance. Differences in public perception of these designs generate various opinions expressed on social media X. This study aims to analyze public sentiment toward the design of Indonesian athletes' uniforms at the Paris 2024 Olympics on social media X and to compare the performance of Naive Bayes Classifier and Support Vector Machine algorithms. The dataset consists of textual data collected from social media X and processed through preprocessing stages and split into training and testing data with an 80:20 ratio. The results show that there are 1,014 positive and 728 negative sentiments. Model evaluation indicates that the Naive Bayes Classifier achieved an accuracy of 80.5%, while the Support Vector Machine achieved 94.2%, outperforming the former. These findings demonstrate that the Support Vector Machine is more effective than the Naive Bayes Classifier for sentiment analysis of social media text data related to the design of Indonesian athletes' uniforms at the Paris 2024 Olympics.

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