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All Journal snimed Telematika : Jurnal Informatika dan Teknologi Informasi Techno LPPM Seminar Nasional Informatika (SEMNASIF) Conference SENATIK STT Adisutjipto Yogyakarta Compiler IKRA-ITH ABDIMAS Journal of Information Systems and Informatics Science in Information Technology Letters Jurnal INFOTEL
Nur Heri Cahyana
Universitas Pembangunan Nasional Veteran Yogyakarta
Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Transformasi Digital pada UMKM Luthfi Handcraft: Meningkatkan Daya Saing dan Branding Produk Melalui Pemasaran Digital Cahyana, Nur Heri; Simanjuntak, Oliver Samuel; Kusumo, Yudhy Widya; Utami, Yenni Sri; Prabowo, Agung
IKRA-ITH ABDIMAS Vol. 9 No. 3 (2025): Jurnal IKRAITH-ABDIMAS Vol 9 No 3 November 2025
Publisher : Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Program Kemitraan Masyarakat (PKM) ini bertujuan untuk mengoptimalkan pemanfaatan kecerdasan buatan (Artificial Intelligence/AI) dalam mempercepat transformasi digital dan meningkatkan daya saing global pada UMKM Luthfi Handcraft di Desa Murtigading, Kabupaten Bantul. Permasalahan utama mitra meliputi rendahnya kualitas visual produk, lemahnya manajemen digital, dan belum optimalnya strategi pemasaran berbasis teknologi. Metode pelaksanaan dilakukan melalui lima tahapan, yakni sosialisasi, pelatihan, penerapan teknologi, pendampingan, dan evaluasi. Hasil kegiatan menunjukkan peningkatan signifikan dalam literasi digital mitra, kemampuan memproduksi konten visual profesional menggunakan AI, penyusunan SOP produksi, serta penguatan strategi digital marketing di marketplace dan media sosial. Program ini memberikan dampak positif berupa peningkatan kapasitas teknologi, kemandirian manajerial, dan perluasan jangkauan pasar. Kegiatan ini sejalan dengan tujuan Sustainable Development Goals (SDGs), sekaligus mendukung pengembangan UMKM berbasis teknologi yang berdaya saing global.
Implementation of Mel-Frequency Cepstral Coefficient as Feature Extraction using K-Nearest Neighbor for Emotion Detection Based on Voice Intonation Nawasta, Revanto Alif; Cahyana, Nur Heri; Heriyanto, Heriyanto
Telematika Vol 20 No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.9518

Abstract

Purpose: To determine emotions based on voice intonation by implementing MFCC as a feature extraction method and KNN as an emotion detection method.Design/methodology/approach: In this study, the data used was downloaded from several video podcasts on YouTube. Some of the methods used in this study are pitch shifting for data augmentation, MFCC for feature extraction on audio data, basic statistics for taking the mean, median, min, max, standard deviation for each coefficient, Min max scaler for the normalization process and KNN for the method classification.Findings/result: Because testing is carried out separately for each gender, there are two classification models. In the male model, the highest accuracy was obtained at 88.8% and is included in the good fit model. In the female model, the highest accuracy was obtained at 92.5%, but the model was unable to correctly classify emotions in the new data. This condition is called overfitting. After testing, the cause of this condition was because the pitch shifting augmentation process of one tone in women was unable to solve the problem of the training data size being too small and not containing enough data samples to accurately represent all possible input data values.Originality/value/state of the art: The research data used in this study has never been used in previous studies because the research data is obtained by downloading from Youtube and then processed until the data is ready to be used for research.
The Evaluation of Effects of Oversampling and Word Embedding on Sentiment Analysis Cahyana, Nur Heri; Fauziah, Yuli; Wisnalmawati, Wisnalmawati; Aribowo, Agus Sasmito; Saifullah, Shoffan
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1077

Abstract

Generally, opinion datasets for sentiment analysis are in an unbalanced condition. Unbalanced data tends to have a bias in favor of classification in the majority class. Data balancing by adding synthetic data to the minority class requires an oversampling strategy. This research aims to overcome this imbalance by combining oversampling and word embedding (Word2Vec or FastText). We convert the opinion dataset into a sentence vector, and then an oversampling method is applied here. We use 5 (five) datasets from comments on YouTube videos with several differences in terms, number of records, and imbalance conditions. We observed increased sentiment analysis accuracy with combining Word2Vec or FastText with 3 (three) oversampling methods: SMOTE, Borderline SMOTE, or ADASYN. Random Forest is used as machine learning in the classification model, and Confusion Matrix is used for validation. Model performance measurement uses accuracy and F-measure. After testing with five datasets, the performance of the Word2Vec method is almost equal to FastText. Meanwhile, the best oversampling method is Borderline SMOTE. Combining Word2Vec or FastText with Borderline SMOTE could be the best choice because of its accuracy score and F-measure reaching 91.0% - 91.3%. It is hoped that the sentiment analysis model using Word2Vec or FastText with Borderline SMOTE can become a high-performance alternative model.
Co-Authors Adhika Noorindra, Adhika Agung Prabowo Agus Sasmito Aribowo Agusdin, Riza Prapascatama Ahmad Taufiq Akbar Arianti, Berliana Andra Awang Hendrianto Pratomo Azizah, Nisa’ul Bambang Yuwono Dreżewski, Rafał Hafsah Hafsah Hari Prapcoyo Herdiansyah, Fachrizal Heriyanto Heriyanto Herlina Jayadianti Husaini, Rochmat Indah, Sika Nur Kusumo, Yudhy Widya Nawasta, Revanto Alif Oliver Samuel Simanjuntak Rifki Indra Perwira Rustamadji, Heru Saifullah, Shoffan Tahalea, Sylvert Prian Tri Mardiana, Tri Wilis Kaswidjanti Wisnalmawati Wisnalmawati Yenni Sri Utami, Yenni Sri Yuli Fauziah