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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming JATI (Jurnal Mahasiswa Teknik Informatika) Melek IT: Information Technology Journal Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Edu Komputika Journal
Mahazam Afrad
Institut Teknologi Telkom Purwokerto
Religion Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Other

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Aspect Based Sentiment Analysis Menggunakan Indobert Model Terhadap Review Pengunjung Objek Wisata Baturraden Febrianto, Dany Candra; Fitriani, Maulida Ayu; Afrad, Mahazam; Khadija, Mutiara Auliya
Melek IT : Information Technology Journal Vol. 10 No. 2 (2024): Melek IT: Information Technology Journal
Publisher : Informatics Department-Universitas Wijaya Kusuma Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30742/melekitjournal.v10i2.358

Abstract

Tourism significantly contributes to regional economic growth and enhances public welfare. Baturraden tourist attraction, located in Banyumas Regency, Central Java, is one of the destinations whose main attraction is nature tourism. Data on visitors to Baturraden tourist attraction over the past few years shows a good trend. To ensure long-term sustainability and enhance service quality, understanding visitor perceptions and experiences is crucial. This study employs Aspect-Based Sentiment Analysis (ABSA) to analyze visitor reviews of Baturraden. Utilizing the IndoBERT model, a deep learning architecture based on Bidirectional Encoder Representations from Transformers (BERT) specifically tailored for the Indonesian language, the research focuses on four key aspects: Attraction, Accessibility, Amenities, and Ancillary Services. Next stage, a pre-processing process is carried out which includes Case Folding, Cleansing, Tokenizing, Normalization, Stemming and Stopword. Model evaluation is conducted using a confusion matrix, assessing accuracy (94.61%), precision (83.22%), recall (96%), and F1-score (88.11). These results demonstrate the model's can classif reviews into the required aspects.A primary challenge encountered in this research involves analyzing reviews exhibiting diverse linguistic styles, including variations in language and dialect, as well as addressing the issue of imbalanced data distribution across the different aspects.
ANALISIS RANTAI PASOK PRODUKSI INDUSTRI JEANS DI PEMALANG MENGGUNAKAN METODE SCOR Sipho Samuel Damanik, Geordy; Restu Saputra, Wildan; Mahya Mafaza, Vicky; Evan Nicholas, Erick; Afrad, Mahazam
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 1 (2025): JATI Vol. 9 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i1.12660

Abstract

Industri jeans di Pemalang merupakan salah satu penggerak utama ekonomi kreatif Indonesia dalam sektor tekstil dan fashion. Kompleksitas rantai pasok dalam industri ini menimbulkan tantangan dalam mengoptimalkan proses dari pengadaan bahan baku hingga distribusi produk ke konsumen. Penelitian ini bertujuan untuk menganalisis dan mengoptimalkan kinerja rantai pasok industri jeans di Pemalang menggunakan metode Supply Chain Operation Reference (SCOR). Metodologi penelitian meliputi pengumpulan data primer dan sekunder mengenai proses pengadaan bahan baku, produksi, inventori, dan distribusi, yang kemudian dianalisis menggunakan lima dimensi utama SCOR: reliability, responsiveness, agility, cost, dan asset management efficiency. Hasil penelitian menunjukkan bahwa kinerja rantai pasok industri jeans di Pemalang mencapai nilai 91,71 dari 100, yang termasuk dalam kategori "Baik Sekali", dengan kapasitas produksi 900- 1000 potong per minggu dan jaringan pemasok yang tersebar di beberapa kota strategis.
Implementation of Natural Language Processing in the Reporting and Handling System of Sexual Violence Cases on Campus Ilwan Syafrinal; Sapta Eka Putra; Mahazam Afrad
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 11 No 2 (2024): Jurnal Ecotipe, October 2024
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jurnalecotipe.v11i2.4511

Abstract

Sexual violence in the campus environment is a serious problem that requires an effective reporting and handling system. This research aims to develop a Natural Language Processing (NLP)-based system that can improve the process of reporting and handling cases of sexual violence on campus. The methodology used includes the application of NLP techniques such as sentiment analysis and entity recognition to automate the identification and handling of reports. The Support Vector Machines (SVM) algorithm is used for the classification of text in this system. The data is collected from various sources, pre-processed, and used to train NLP models. The results of the study show that the system developed has an accuracy level of 91%, precision of 93%, and recall of 87%, which illustrates its effectiveness in collecting reports of sexual violence anonymously and accurately. Feedback from early adopters shows that the system improves the efficiency and accuracy of the reporting process. The conclusion of this study is that the implementation of NLP can significantly improve the reporting and handling system of sexual violence on campus. Further research is suggested to expand the scope of the system and improve its analysis capabilities.
Obesity Status Prediction Through Artificial Intelligence and Balanced Label Distribution Using SMOTE Riyandi, Arif; Mahazam Afrad; M Yoka Fathoni; Yogo Dwi Prasetyo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6204

Abstract

Obesity, a global health challenge influenced by genetic and environmental factors, is characterized by excessive body fat that increases the risk of various diseases. With over two billion individuals affected worldwide, addressing this issue is crucial. This study investigated the application of Artificial Intelligence (AI) to predict obesity status using a dataset of 1,610 individuals, including demographic and anthropometric data. Four AI algorithms were analyzed: Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Random Forest, and Support Vector Machine (SVM). The Synthetic Minority Over-Sampling Technique (SMOTE) was applied to address dataset imbalance. The results demonstrate that SMOTE significantly enhanced the models' performance, especially in recall and F1-score for minority classes, such as obesity. Random Forest achieved the highest accuracy (92%) and recall (92%) post-SMOTE. The ANN showed substantial improvement in recall, increasing from 77% to 89%, whereas the SVM achieved the highest precision (89%), minimizing false positives. Despite these improvements, KNN remained the least effective. The findings underscore the critical role of SMOTE in improving AI model accuracy for obesity prediction and highlight Random Forest as the most reliable algorithm for clinical decision-making. Limitations, such as dataset representativeness, suggest future research directions, including expanding data diversity and advanced feature selection techniques. This study provides valuable insights into leveraging AI and preprocessing methods for obesity management.
Inisiasi Teknologi Tepat Guna: Pembuatan Eco Enzyme dari Kulit Buah untuk Skema Barter Limbah Rumah Tangga Melalui Aplikasi Digital Aldo, Dasril; Kurniawati, Ajeng Dyah; Lishobrina, Lina Fatimah; Paramadini, Adanti Wido; Afrad, Mahazam; Fathan, Faizal Burhani Ulil; Maulana, Ihsan; Sulaeman, Gilang; Yasin, Feri
Jurnal Pengabdian Masyarakat: Pemberdayaan, Inovasi dan Perubahan Vol 5, No 3 (2025): JPM: Pemberdayaan, Inovasi dan Perubahan
Publisher : Penerbit Widina, Widina Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59818/jpm.v5i3.1599

Abstract

Household organic waste, which accounts for more than 60% of total national waste in Indonesia, remains a major challenge in community-based waste management. This community service program aims to raise public awareness and engagement through the production of eco enzyme made from fruit peels as an environmentally friendly product, which will later become part of a waste exchange (barter) system using a digital application. The activity involved 25 participants consisting of volunteers, university students, and members of environmental communities. The methods included socialization, hands-on training on eco enzyme production using a 3:1:1 ratio of water, brown sugar, and fruit peels, followed by a 90-day fermentation process. Evaluation was conducted through pre- and post-training questionnaires and direct observation of participants’ practices. Results showed that 88% of participants successfully produced eco enzyme with an optimal pH level (3.5–4.5), and 76% expressed interest in joining the proposed digital barter system. The activity also improved participants’ understanding of circular economy concepts and independent waste management. In conclusion, the eco enzyme training serves as a strategic initial step in developing a waste exchange ecosystem based on appropriate technology, while also acting as an educational tool to foster environmental awareness through participatory and sustainable approaches.ABSTRAKPermasalahan limbah organik rumah tangga, yang mencapai lebih dari 60% dari total sampah nasional, masih menjadi tantangan utama dalam pengelolaan sampah berbasis masyarakat. Kegiatan pengabdian ini bertujuan untuk meningkatkan kesadaran dan keterlibatan masyarakat melalui pembuatan eco enzyme dari kulit buah sebagai produk ramah lingkungan, yang kelak akan dijadikan bagian dari sistem pertukaran (barter) dengan limbah rumah tangga menggunakan aplikasi digital. Kegiatan dilaksanakan pada tanggal 4 Mei 2025 di Desa Muntang, Kabupaten Purbalingga, dengan melibatkan 25 peserta yang terdiri dari relawan, mahasiswa, dan anggota komunitas peduli lingkungan. Mitra kegiatan ini adalah Komunitas Limbah Pustaka yang turut berperan dalam penyediaan lokasi dan fasilitasi peserta. Metode yang digunakan meliputi sosialisasi, pelatihan teknis pembuatan eco enzyme dengan komposisi 3 bagian air, 1 bagian gula merah, dan 1 bagian kulit buah, seperti kulit nanas, jeruk, semangka, dan pepaya, serta pendampingan fermentasi selama 90 hari. Evaluasi dilakukan melalui kuesioner pre dan post pelatihan, serta observasi praktik langsung. Hasil kegiatan menunjukkan bahwa 88% peserta berhasil memproduksi eco enzyme dengan kualitas pH optimal (3,5–4,5), dan 76% di antaranya menyatakan tertarik untuk mengikuti skema barter digital berbasis aplikasi. Kegiatan ini juga meningkatkan pemahaman peserta terhadap konsep ekonomi sirkular dan pengelolaan limbah mandiri. Kesimpulannya, pelatihan pembuatan eco enzyme merupakan langkah awal yang strategis dalam membangun ekosistem pertukaran limbah berbasis teknologi tepat guna, sekaligus menjadi media edukatif dalam meningkatkan kepedulian terhadap lingkungan secara partisipatif dan berkelanjutan.
Pemanfaatan TikTok sebagai Media Digital Marketing untuk Pemasaran Produk UMKM Desa Cingebul Wiedanto Prasetyo, Muhamad Awiet; Fathoni, M. Yoka; Safitri, Sisilia Thya; Fernandez, Sandhy; Wijayanto, Sena; Prasetyo, Yogo Dwi; Afrad, Mahazam
Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Vol 8, No 3 (2025): Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstormin
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/japhb.v8i3.8910

Abstract

Transformasi digital di sektor UMKM perdesaan masih menghadapi berbagai tantangan, terutama dalam hal pemanfaatan media sosial sebagai sarana pemasaran. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan kapasitas digital marketing pelaku UMKM di Desa Cingebul melalui pelatihan dan pendampingan pemanfaatan TikTok sebagai platform promosi. Metode pelaksanaan terdiri atas pemetaan kebutuhan, pelatihan teknis, praktik pembuatan konten, dan evaluasi performa. Hasil kegiatan menunjukkan adanya peningkatan signifikan dalam hal kepercayaan diri peserta, keterampilan membuat konten, serta konsistensi dalam membangun citra produk secara digital. Selain itu, muncul pula individu yang berperan sebagai pemimpin lokal (local leader) dalam mendampingi UMKM lain, yang menjadi indikator awal terbentuknya ekosistem digital desa. Temuan ini menguatkan pentingnya pendekatan partisipatif dan kontekstual dalam penguatan literasi digital masyarakat perdesaan.
Sentiment Analysis of Visitor Reviews on Baturaden Tourist Attraction Using Machine Learning Methods Afrad, Mahazam; Febrianto, Dany Candra; Wijayanto, Sena; Fathoni, M. Yoka
Edu Komputika Journal Vol. 11 No. 1 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i1.10561

Abstract

This study evaluates the performance of four machine learning models: Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes in analyzing visitor reviews of the Lokawisata Baturaden tourist attraction. Using 5-fold cross-validation, the study aims to determine which machine learning model best suits sentiment analysis on the Baturaden review data. This study was conducted through several stages, including data preprocessing, feature extraction, and the data training process. Case folding, text cleaning, tokenization, stopword removal, and stemming were performed during the data preprocessing stage. The feature extraction method used was TF-IDF. SMOTE was applied to increase data variation and address the data imbalance in the dataset. The results show that SVM provides the best performance with an accuracy of 0.937, an F1-score of 0.937, a precision of 0.943, and a recall of 0.937. Random Forest also performs well with an accuracy of 0.918 and an F1-score of 0.918, though slightly below SVM. KNN shows the lowest performance with an accuracy of 0.651 and an F1-score of 0.544, while Naive Bayes performs adequately with an accuracy of 0.845 and an F1-score of 0.841. Based on this evaluation, SVM is recommended as the best model for sentiment analysis of reviews, followed by Random Forest as a good alternative. The KNN model is not recommended due to its lower performance, while Naive Bayes can be considered for its speed and simplicity, although its results are not as good as SVM and Random Forest. These conclusions guide the selection of the optimal model to enhance understanding and visitor experience at the Baturaden tourist attraction.
Co-Authors Adanti Wido Paramadini Ajeng Dyah Kurniawati Arif Riyandi Dany Candra Febrianto Dasril Aldo Evan Nicholas, Erick Fathan, Faizal Burhani Ulil Fitriani, Maulida Ayu Ilwan Syafrinal Khadija, Mutiara Auliya Lina Fatimah Lishobrina M Yoka Fathoni Mahya Mafaza, Vicky Maulana, Ihsan Muhamad Awiet Wiedanto Prasetyo Muljono Muljono Pujiono Pujiono Restu Saputra, Wildan Sandhy Fernandez Sapta Eka Putra Sipho Samuel Damanik, Geordy Sisilia Thya Safitri Sulaeman, Gilang Yasin, Feri Yogo Dwi Prasetyo