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All Journal Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Journal Of Artificial Intelligence And Software Engineering
Cipto Utomo, Bangun Prajadi
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Computer Science & IT

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Pengaruh Kualitas Pelayanan Melalui Teknologi Informasi Dan Kualitas Produk Terhadap Keputusan Pembelian di CV Pustaka Bengawan Cipto Utomo, Bangun Prajadi; Santosa, Tri Djoko
Jurnal Informa : Jurnal Penelitian dan Pengabdian Masyarakat Vol 9 No 2 (2023): Desember
Publisher : Politeknik Indonusa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46808/informa.v9i2.255

Abstract

This study seeks to assess the impact of both service quality through information technology and product quality on purchasing decisions, examining these influences both individually and collectively. The research was carried out among customers of CV Pustaka Bengawan, employing a quantitative causal analysis to scrutinize the independent variables—product quality and service via information technology—in relation to the dependent variable, namely, purchasing decisions. Data collection involved the distribution of questionnaires to 100 respondents, and subsequent analysis utilized multiple linear regression, t-test, F-test, and coefficient of determination. The findings reveal a positive and statistically significant influence of both product quality and service through information technology on purchasing decisions, both in isolated and combined contexts. The combined contribution of product quality and service via information technology to purchasing decisions is estimated at 63.7%, with the remaining 36.3% being attributable to other variables. Therefore, companies are expected to improve their service quality through information technology to increase purchasing decisions. Additionally, they should improve product quality, especially for products that are already accepted and in demand by consumers. Future researchers can conduct research on purchasing decisions using different approaches and indicators based on different theories.
Analisis Dampak Chatgpt Terhadap Kreativitas Dan Produktivitas Siswa Dalam Penulisan Naskah Broadcasting Suryani, Fajar; Cipto Utomo, Bangun Prajadi
Jurnal Informa : Jurnal Penelitian dan Pengabdian Masyarakat Vol 10 No 2 (2024): Desember
Publisher : Politeknik Indonusa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46808/informa.v10i2.276

Abstract

Penelitian ini bertujuan untuk menganalisis dampak penggunaan teknologi AI, khususnya ChatGPT, dalam penulisan naskah broadcasting pada penugasan siswa. Permasalahan yang dihadapi adalah bagaimana AI dapat meningkatkan kreativitas dan produktivitas siswa dalam penulisan naskah, sekaligus mengidentifikasi tantangan dan keterbatasan yang timbul dalam penerapannya. Penelitian ini menggunakan metode kajian literatur, mengumpulkan dan menganalisis berbagai jurnal yang membahas penerapan AI dalam dunia broadcasting dan pendidikan. Hasil penelitian menunjukkan bahwa penggunaan AI dalam penulisan naskah dapat meningkatkan efisiensi, memperkaya ide kreatif, dan mempercepat proses produksi naskah. Namun, tantangan terkait etika, bias algoritma, dan keterbatasan pemahaman konteks dalam AI tetap menjadi masalah yang perlu diatasi. Kesimpulan utama adalah bahwa meskipun AI dapat mendukung siswa dalam menghasilkan naskah yang lebih variatif dan cepat, pengawasan manusia tetap diperlukan untuk menjaga kualitas, orisinalitas, dan relevansi naskah yang dihasilkan. Penelitian ini memberikan wawasan tentang potensi besar dan tantangan AI dalam pendidikan broadcasting
Sentiment Analysis of Fans Toward Brand Merchandise Releases Using Support Vector Machine (SVM) Munaiseche, Christian Imanuel; Nurchim, Nurchim; Cipto Utomo, Bangun Prajadi
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7264

Abstract

The release of merchandise by idol groups often sparks various emotional reactions among fans, particularly on social media platforms. This study investigates fan sentiment regarding the birthday merchandise release by JKT48 members on the X (Twitter) platform using the Support Vector Machine (SVM) algorithm. A total of 1,062 comments were collected using the Tweet Harvest tool and manually categorized into three sentiment classes: positive, neutral, and negative. The collected data underwent several pre-processing stages, including case folding, data cleansing, tokenization, and stopword removal. The text data were then transformed into numerical features using the Term Frequency-Inverse Document Frequency (TF-IDF) method. To address the class imbalance issue, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Experimental results show that the SVM model without SMOTE achieved an accuracy of 84.62% and an F1-score of 76.79%. After applying SMOTE, model performance improved significantly, with accuracy reaching 90.09% and F1-score increasing to 90.15%. Furthermore, the results of 5-fold cross-validation confirmed the positive impact of SMOTE in enhancing the model's ability to classify sentiment, particularly for underrepresented classes.
Comparative Analysis of Classification Models for Sales Prediction in E-commerce: Decision Tree, Random Forest, SVM, Naive Bayes, and KNN Purwanto, Eko; Cipto Utomo, Bangun Prajadi; Permatasari, Hanifah; Mohd, Farahwahida
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5224

Abstract

The swift expansion of e-commerce has markedly heightened the necessity for precise sales forecasting, essential for efficient marketing tactics and inventory control. This research evaluates five classification models—Decision Tree, Random Forest, Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbors (KNN)—to predict sales outcomes using e-commerce transaction data. The models were assessed utilizing criteria including accuracy, precision, recall, F1-score, AUC, and Log Loss. The findings indicate that Random Forest exceeds the performance of the other models, with an accuracy of 97.5% and an AUC of 0.991, markedly outperforming the alternatives. This study presents a unique contribution by contrasting these classification models in the realm of e-commerce in Indonesia, yielding significant insights for the advancement of more effective predictive algorithms in informatics. The results not only enhance the optimization of marketing strategies but also enrich the comprehension of machine learning applications in sales forecasting. This study underscores the necessity of choosing the appropriate model for enhanced sales forecasting, with considerable ramifications for data-driven decision-making in the e-commerce sector.
Co-Authors Eko Purwanto Mohd, Farahwahida Munaiseche, Christian Imanuel Nurchim Nurchim Permatasari, Hanifah Santosa, Tri Djoko Suryani, Fajar