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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Ilmiah Media Sisfo Budimas : Jurnal Pengabdian Masyarakat Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Pengabdian Masyarakat Unama Prosiding Seminar Nasional Ilmu Teknik
Rohaini, Eni
Universitas Dinamika Bangsa
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Optimalisasi Digital Marketing Dan Bisnis Digital Dalam Ruang Kreatif Untuk Meningkatkan Potensi Pemuda Di Desa Tantan Kecamatan Sekernan Kabupaten Muaro Jambi Nurhayati; Nurhadi; Dodo Zaenal Abidin; Yossinomita; Eni Rohaini; Meisak, Despita; Sharipuddin; Beni Purnama; Roby Setiawan8; ronal naibaho; Ayu Feranika
Jurnal Pengabdian Masyarakat UNAMA Vol 4 No 2 (2025): JPMU Volume 4 Nomor 2 Oktober 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jpmu.2025.4.2.2543

Abstract

Pelatihan ini bertujuan untuk memberikan pemahaman dan keterampilan praktis mengenai manfaat digital marketing dan bisnis digital dalam ruang kreatif sebagai upaya meningkatkan potensi pemuda di Desa Tantan, Kecamatan Sekernan, Kabupaten Muaro Jambi. Kegiatan dilaksanakan pada tanggal 16 Agustus 2026 bertempat di Aula Masyarakat Desa Tantan, dengan jumlah peserta sebanyak 20 orang yang terdiri dari pemuda desa, anggota Karang Taruna, Koperasi Desa, BUMDes, Perpustakaan Desa, serta dihadiri oleh Kepala Desa Bapak Mashur, S.Pd. Metode pelatihan meliputi penyampaian materi, diskusi interaktif, dan praktik langsung mengenai strategi digital marketing, pemanfaatan media sosial, serta pengembangan bisnis digital berbasis potensi lokal. Hasil dari pelatihan menunjukkan adanya peningkatan pemahaman peserta mengenai pentingnya literasi digital, keterampilan pemasaran berbasis teknologi, serta kreativitas dalam membangun brand desa. Selain itu, pelatihan ini juga memperkuat sinergi antarorganisasi desa sebagai upaya penguatan kelembagaan dalam pengembangan ekonomi kreatif berbasis digital.Dengan demikian, pelatihan ini diharapkan dapat menjadi langkah awal dalam membentuk pemuda yang berdaya saing, mandiri, serta mampu berkontribusi terhadap pembangunan ekonomi desa secara berkelanjutan di era digital
Fitur Information Gain untuk Meningkatkan Nilai Performa Pengklasifikasi Machine Learning pada Analisis Sentimen Komentar Spam Pengguna Youtube Jasmir, Jasmir; Gunardi, Gunardi; Rohaini, Eni; Naibaho, Ronald; Sukoco, Bambang; Jasmir , Jasmir
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 2: April 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.132

Abstract

Perkembangan pesat media sosial telah memberikan ruang bagi setiap individu untuk menyampaikan pendapat, baik berupa komentar positif maupun negatif terhadap konten yang mereka akses. Kemudahan dalam memberikan opini secara daring ini berdampak pada semakin besarnya jumlah ulasan yang tersedia. Namun, volume ulasan yang sangat besar sering kali sulit untuk dianalisis secara manual dan berpotensi menimbulkan bias dalam penilaian. Untuk mengatasi permasalahan tersebut, diperlukan pendekatan otomatis melalui klasifikasi sentimen yang bertujuan mengelompokkan opini pengguna ke dalam kategori positif atau negatif. Dalam penelitian ini digunakan tiga algoritma pembelajaran mesin, yaitu Naïve Bayes (NB), K-Nearest Neighbor (KNN), dan Random Forest (RF). Data penelitian diperoleh dari public dataset UCI Machine Learning. Fokus penelitian adalah meningkatkan kinerja klasifikasi dengan memanfaatkan teknik seleksi fitur information gain. Hasil eksperimen menunjukkan bahwa penerapan information gain secara konsisten meningkatkan performa semua algoritma yang diuji, baik pada metrik akurasi, presisi, recall, maupun f1-score. Naïve Bayes awalnya memperoleh akurasi tertinggi sebesar 74,33% pada kondisi tanpa fitur tambahan. Namun, setelah penerapan information gain, algoritma KNN menunjukkan hasil paling optimal dengan akurasi mencapai 81,28% serta performa yang relatif seimbang pada semua metrik evaluasi. Sementara itu, Random Forest juga mengalami peningkatan, meskipun tidak melampaui KNN. Secara keseluruhan, penelitian ini menegaskan bahwa pemilihan fitur yang relevan melalui information gain mampu meningkatkan efisiensi dan efektivitas klasifikasi sentimen, serta dapat menjadi pendekatan yang potensial untuk menganalisis opini dalam skala besar.   Abstract The rapid growth of social media has provided individuals with the opportunity to freely express their opinions, whether positive or negative, toward the content they encounter. The increasing ease of sharing opinions online has resulted in a massive volume of user reviews. However, the large number of reviews is difficult to analyze manually and may introduce bias in interpretation. To address this issue, sentiment classification is applied to automatically categorize user opinions into positive or negative classes. In this study, three machine learning algorithms were employed: Naïve Bayes (NB), K-Nearest Neighbor (KNN), and Random Forest (RF). The dataset was obtained from the public UCI Machine Learning repository. The main objective of this research is to improve classification performance by utilizing feature selection through the information gain method. Experimental results demonstrate that applying information gain consistently enhances the performance of all evaluated algorithms across multiple metrics, including accuracy, precision, recall, and F1-score. Without feature selection, Naïve Bayes achieved the highest accuracy of 74.33%. However, after applying information gain, KNN outperformed the other algorithms by reaching an accuracy of 81.28% and exhibited balanced results across all evaluation metrics. Random Forest also showed improvement but did not surpass the performance of KNN. Overall, these findings highlight the importance of feature selection in improving both the efficiency and effectiveness of sentiment classification. Furthermore, the use of information gain proves to be a promising approach for large-scale opinion analysis, particularly in handling the high dimensionality of textual data.
Word Embedding Feature for Improvement Machine Learning Performance in Sentiment Analysis Disney Plus Hotstar Comments Jasmir Jasmir; Nurhadi Nurhadi; Eni Rohaini; M Riza Pahlevi B; Daniel Sintong Pardamean Simanjuntak
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28799

Abstract

In this research we apply several machine learning methods and word embedding features to process social media data, specifically comments on the Disney Plus Hotstar application. The word embedding features used include Word2Vec, GloVe, and FastText. Our aim is to evaluate the impact of these features on the classification performance of machine learning methods such as Naive Bayes (NB), K-Nearest Neighbor (KNN), and Random Forest (RF). NB is very simple and efficient and very sensitive to feature selection. Meanwhile, KNN is known for its weaknesses such as biased k values, overly complex computations, memory limitations, and ignoring irrelevant attributes. Then RF has a weakness, namely that the evaluation value can change significantly with just a slight change in the data. Feature selection in text classification is crucial for enhancing scalability, efficiency, and accuracy. Our testing results indicate that KNN achieved the highest accuracy both before and after feature selection. The FastText feature led to the highest performance for KNN, yielding balanced accuracy, precision, recall, and F1-score values.
Evaluasi Kinerja Machine Learning pada Klasifikasi Penyakit Jantung Menggunakan Teknik Penyeimbangan Data Eni Rohaini; Gunardi, Gunardi; Nurhayati Nurhayati; Jasmir Jasmir; Zahra Prisdian Tiararosa
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.59

Abstract

AImbalanced data remains a significant issue in heart disease classification using machine learning, as it tends to cause models to overestimate the majority class while ignoring minority classes with high clinical value. This can lead to a decrease in accuracy and the model's ability to accurately detect disease cases. Therefore, this study aims to assess the effectiveness of oversampling techniques, namely Random Oversampling and Synthetic Minority Oversampling Technique (SMOTE), in improving the performance of the K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF) algorithms. The dataset used comes from Kaggle and consists of 918 data sets with 12 attributes representing patient information related to heart disease prediction. The research stages include data preprocessing, baseline model testing, and re-evaluation using the two oversampling methods. Experimental results show that oversampling can improve the performance of all algorithms. KNN achieved the best results with SMOTE, with an accuracy of 72.98% and an F1-score of 75.39%. In the Naive Bayes algorithm, both oversampling techniques produced relatively stable performance, with the highest F1-score of 73.56% using SMOTE. Meanwhile, Random Forest showed the most optimal performance when combined with Random Oversampling, with an accuracy of 79.19% and an F1-score of 81.51%. These findings confirm that the success of data balancing techniques is strongly influenced by the characteristics of the classification algorithm used, and provide a practical contribution in determining strategies for handling imbalanced data in health research.
An Adaptive Feature-Aware Hybrid Resampling Strategy for Imbalanced Diabetes Classification with Integrated Balanced Index Evaluation Jasmir Jasmir; Riza Pahlevi; Gunardi Gunardi; Eni Rohaini; Tiko Nur Annisa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7418

Abstract

Class imbalance remains a critical challenge in medical data classification, particularly in diabetes prediction, as it significantly degrades minority-class sensitivity. This study proposes an Adaptive Feature-Aware Hybrid Resampling Strategy (AHRS) that dynamically integrates oversampling and undersampling based on Imbalance Ratio (IR) and Feature Importance (FI). Unlike conventional static resampling methods, AHRS iteratively adjusts class distribution while preserving informative feature structures. In addition, this study introduces the Integrated Balanced Index (IBI), a bounded composite metric integrating precision, recall, and specificity to provide a fairer evaluation of classification performance on imbalanced medical datasets. The proposed approach was evaluated using the Pima Indian Diabetes Dataset (768 instances) with K-Nearest Neighbor, Naïve Bayes, and Random Forest classifiers under 5-fold stratified cross-validation. Experimental results demonstrate that AHRS consistently outperforms SMOTE, Random Oversampling, and Tomek Links, achieving accuracy improvements of 5–7% and recall gains of up to 10%. Random Forest combined with AHRS achieved the highest IBI score of 0.90, indicating strong balance between sensitivity and specificity. The findings suggest that adaptive, feature-aware resampling combined with balanced evaluation metrics provides a reliable and interpretable framework for fair medical classification systems and Clinical Decision Support Systems (CDSS).
PELATIHAN IMPLEMENTASI TEXT-TO-IMAGE AI UNTUK DESAIN LOGO UMKM KOTA JAMBI Setiawan, Roby; Ismail, Muhammad; Nugroho, Agus; Rohaini, Eni; Assegaff, Setiawan; Nurhadi; Aryani, Lies; Meisak, Despita
Jurnal Pengabdian Masyarakat UNAMA Vol 5 No 1 (2026): JPMU Volume 5 Nomor 1 April 2026
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jpmu.2026.5.1.2767

Abstract

Usaha Mikro, Kecil, dan Menengah (UMKM) memiliki peran strategis dalam pertumbuhan ekonomi daerah, namun masih menghadapi keterbatasan dalam membangun identitas visual yang profesional, khususnya dalam pembuatan logo usaha. Biaya desain profesional yang relatif tinggi menjadi kendala bagi sebagian pelaku UMKM. Perkembangan teknologi Artificial Intelligence (AI), khususnya Text-to-Image AI, menawarkan solusi alternatif yang cepat, terjangkau, dan kreatif dalam menghasilkan desain visual berbasis deskripsi teks. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan pemahaman dan keterampilan pelaku UMKM di Kota Jambi dalam memanfaatkan teknologi Text-to-Image AI untuk pembuatan logo usaha. Metode pelaksanaan menggunakan pendekatan partisipatif melalui tahapan pre-test, penyampaian materi, demonstrasi, praktik langsung, dan post-test. Evaluasi dilakukan menggunakan skala Likert 1–5 terhadap 17 peserta. Hasil menunjukkan peningkatan rata-rata pemahaman dari 2,47 pada pre-test menjadi 4,38 pada post-test. Peningkatan signifikan terjadi pada indikator kemampuan menyusun prompt dan kepercayaan diri menggunakan logo berbasis AI. Kegiatan ini menunjukkan bahwa pelatihan berbasis praktik AI efektif dalam meningkatkan kapasitas branding digital UMKM secara mandiri dan berkelanjutan.
Co-Authors Abdurrahman Ade Saputra Agus Nugroho, Agus Agus Siswanto Aldino Ramadhani Amin Syam, Atha Bugis Andrianti, Ari Aryani, Lies Ayu Alysia Bambang Sukoco Beni Purnama Daniel Sintong Pardamean Simanjuntak Despita Meisak Dodi Sandra Dodo Zaenal Abidin Errissya Rasywir Fadillah, Yosi Feranika, Ayu Gunardi Gunardi Gunardi Hendri Hendri Ibnu Sani Wijaya Indana Arum , Refi Inggrit Tia Pratiwi Inggrit Tia Septiana Jasmir Jasmir , Jasmir Jasmir Jasmir Jasmir, Jasmir Joni Devitra, Joni Jovansah, Afif Lely Febriyanti Lies Aryani Lola Yorita Astri, Lola Yorita M Riza Pahlevi M Riza Pahlevi B M. Taufik Aufa Meisak, Despita mery Moh. Ismail Muhammad Afif Dzaky Khairullah Muhammad Fadri Adam Muhammad Fajar Muhammad Riza Pahlevi Muhammad Rizki Saputra NAIBAHO, RONALD Nasrul Ahlunaza NURHADI Nurhadi Nurhadi Nurhadi Nurhadi Nurhayati Nurhayati Nurhayati Nurhayati Nurhayati Oktavianus, Welsen Oldie Versileno, Satria Pahlevi, M. Riza Purnama, Benni Putra Ramadhan, Muhammad Alif Ramadhan Saputra, Tri reza saputra, Fharis Rezky Pramudia, Muhammad Rico Natanael Rifky Januardi Risky Handayani Riza Pahlefi Riza Pahlevi, Riza ROBY SETIAWAN ronal naibaho Setiawan Assegaf Sharipuddin Silvia Rianti Agustini Sirait, Gusnaldi Triardo Stevent Chandra Dinata The, Felix Filbert Tiko Nur Annisa Virginia Casanova Andiko William William WILLY RIYADI Xaverius Sika Yossinomita Yovi Pratama Zahra Prisdian Tiararosa