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All Journal Medula EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Prosiding SNATIF Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Applied Information, Communication and Technology (JAICT) Syntax Literate: Jurnal Ilmiah Indonesia JurTI (JURNAL TEKNOLOGI INFORMASI) Jiko (Jurnal Informatika dan komputer) JURIKOM (Jurnal Riset Komputer) KOMPUTA : Jurnal Ilmiah Komputer dan Informatika International Journal of Islamic Business and Economics (IJIBEC) RISTEK : Jurnal Riset, Inovasi, dan Teknologi Kabupaten Batang Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Litbang Kota Pekalongan KLIK: Kajian Ilmiah Informatika dan Komputer RISTEK: Jurnal Riset, Inovasi dan Teknologi Kabupaten Batang IC Tech: Majalah Ilmiah SERVIRISMA: Jurnal Pengabdian kepada Masyarakat Jurnal Surya Informatika Journal Of Artificial Intelligence And Software Engineering International Journal of Islamic Business and Economics (IJIBEC) International Journal of Information Technology and Business Computing and Information System Journal Dedikasi Nusantara Bestikom: Jurnal Pengabdian Kepada Masyarakat IC Tech: Majalah Ilmiah
Susanto, Eko Budi
Institut Widya Pratama
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Neuroscience Public Health Social Sciences Other

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PENGEMBANGAN CHATBOT UNTUK LAYANAN PENERIMAAN MAHASISWA BARU DI INSTITUT WIDYA PRATAMA DENGAN MENGGUNAKAN METODE NAIVE BAYES Susanto, Eko Budi; Mohammad Reza Maulana; Arochman
IC Tech: Majalah Ilmiah Vol 20 No 1 (2025): IC Tech: Majalah Ilmiah Volume XX No. 1 April 2025
Publisher : P3M Institut Widya Pratama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47775/ictech.v20i1.318

Abstract

In this study, a chatbot for new student admissions was developed using the Naïve Bayes algorithm. The data source used was a dialogue between admissions staff and prospective students, which was based on information from the new student admissions brochure of Institut Widya Pratama (formerly STMIK Widya Pratama) for the 2024-2025 academic year. The data was arranged in JSON format, including three elements: question structure, classification, and answers. Data pre-processing steps included text normalization, label encoding, vectorization, and word embedding. The Naïve Bayes algorithm was chosen to train the model because of its ability to predict responses from discrete data. The test results showed that this algorithm achieved an accuracy level of 88%, surpassing the performance of the K-Neighbors-Classifier algorithm. The resulting model was implemented in a desktop-based chatbot application that was run via the command prompt, using the Python programming language and several libraries such as pickle, string, and numpy. This application is able to provide answers to user questions by accessing the model stored in the *.pkl file. The results of the study show that the Naïve Bayes algorithm is effective in predicting user questions, with sufficient accuracy for a new student admissions chatbot application.
Klasifikasi Jenis Burung Berdasarkan Suara Kicau Menggunakan Ekstraksi MFCC dan BiLSTM Janah, Roikhatul; Susanto, Eko Budi; Setiawan, Tri Agus
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i2.17694

Abstract

Automatic bird species classification based on chirping sounds has become an important solution to support conservation efforts for Indonesia's biodiversity, which comprises 1.835 bird species. This study proposes a classification system that combines Mel-Frequency Cepstral Coefficients (MFCC) feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) architecture to identify 10 commonly found Indonesian bird species. The research dataset utilized 750 bird sound recordings from the xeno-canto.org platform, segmented into 4-second duration clips and augmented to 3,750 samples through pitch shift and time stretch techniques. MFCC feature extraction with 40 coefficients was employed to represent the spectral characteristics of bird sounds, while the BiLSTM model was selected to capture complex bi-directional temporal dependencies in bird vocal signals. In the testing process, an 80:20 data split was performed for training and testing. Confusion matrix analysis confirms the model's capability to distinguish unique characteristics of each species with minimal error rates. Research results demonstrate that the system achieved a classification accuracy of 98%. The combination of MFCC and BiLSTM proves effective for automated and sustainable biodiversity monitoring and bird conservation applications in Indonesia.
IMPROVING INDONESIAN SPEECH EMOTION CLASSIFICATION USING MFCC AND BILSTM WITH AUDIO AUGMENTATION Muhammad Septiyanto; Eko Budi Susanto; Devi Sugianti
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 3 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10820

Abstract

Emotion classification from speech has become an important technology in the modern artificial intelligence era. However, research for the Indonesian language is still limited, with existing methods predominantly relying on conventional machine learning approaches that achieve a maximum accuracy of only 90%. These traditional methods face challenges in capturing complex temporal dependencies and bidirectional contextual patterns inherent in emotional speech, particularly for Indonesian prosodic characteristics. To address this limitation, this study uses a combination of Mel-Frequency Cepstral Coefficients (MFCC) feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) model with audio augmentation techniques for Indonesian speech emotion classification. The IndoWaveSentiment dataset contains 300 audio recordings from 10 respondents with five emotion classes: neutral, happy, surprised, disgusted, and disappointed. Audio augmentation techniques with a 2:1 ratio using five methods generated 900 samples. MFCC feature extraction produced 40 coefficients that were processed using BiLSTM architecture with two bidirectional layers (256 and 128 units). The model was trained using Adam optimizer with early stopping. Research results show the highest accuracy of 93.33% with precision of 93.7%, recall of 93.3%, and F1-score of 93.3%. The "surprised" emotion achieved perfect performance (100%), while "happy" had the lowest accuracy (88.89%). This result surpasses previous benchmarks on the same dataset, which utilized Random Forest (90%) and Gradient Boosting (85%). This study demonstrates the effectiveness of combining MFCC, BiLSTM, and audio augmentation in capturing Indonesian speech emotion characteristics for the development of voice-based emotion recognition systems.
A Diagnostic Framework for Staged AI Adoption in Batik Motif Recognition: Integrating CNN Evidence and Implementation Readiness Irwan Sembriring; Paminto Agung Christianto; Eko Budi Susanto; Suharyadi Suharyadi; Cheryl Louisa Loedwyca
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1427

Abstract

This study proposes a diagnostic dual-layer decision-support framework for staged artificial intelligence adoption in batik motif recognition. The objective is to examine whether technical evidence from convolutional neural network classification and perceived implementation-side readiness can be jointly interpreted to prevent premature deployment in cultural-heritage recognition. The contribution of the study is not a new classifier architecture, but an operational diagnostic logic that treats model performance, class-level instability, readiness perception, governance, security, and feedback mechanisms as complementary but non-substitutable evidence. Methodologically, the technical layer evaluated three transfer-learning baselines, namely VGGNet-16, ResNet50, and MobileNetV2, using 983 batik images across 20 motif classes. The implementation layer assessed perceived readiness among 173 information technology practitioners using the Technology-Organization-Environment-Human plus Feedback dimensions. The integration layer then mapped technical-readiness evidence and readiness perception into explicit staged-adoption decisions rather than averaging them as interchangeable indicators.  The analysis used performance summaries, readiness profiles, decision matrices, security checklists, learning curves, and confusion-matrix diagnostics to connect empirical observations with staged adoption recommendations. The best-performing baseline was ResNet50, with 45% accuracy and a macro F1-score of 0.40, showing low technical readiness and substantial motif-specific instability. In contrast, the readiness survey indicated high perceived implementation-side readiness, with an average agreement score of 78.7%. This mismatch reveals a readiness asymmetry: implementation support may exist even when the recognition model remains technically immature. The findings imply that batik-recognition systems should prioritize dataset expansion, expert label validation, model refinement, moderated feedback, security governance, and controlled pilot testing before operational deployment. The framework provides a transparent basis for risk-aware, staged adoption decisions in artificial-intelligence-assisted cultural heritage preservation.
Business Intelligence untuk Segmentasi Pelanggan dengan Metode K-Means di Mizumi Onsen M. Kemal Aditya Hananta; Eko Budi Susanto; Devi Sugianti
EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Vol 15, No 2 (2025): December
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/expert.v15i2.4452

Abstract

Strategi pemasaran yang lebih responsif dan berbasis data sangat penting untuk sektor perhotelan mengingat persaingan yang semakin ketat. Mizumi Onsen sebagai hotel onsen yang terletak di Wonosobo, meskipun memiliki banyak informasi tentang pelanggan, belum berhasil memanfaatkan data tersebut secara optimal untuk mendapatkan segmentasi pasar yang akurat. Penelitian ini bertujuan untuk menggunakan algoritma K-Means Clustering yang dikombinasikan dengan Business Intelligence, tidak hanya untuk mengenali segmen pelanggan, tetapi juga untuk memperbaiki strategi pemasaran digital agar lebih efisien dan tepat sasaran. Data dalam penelitian ini mencakup 1.049 transaksi pelanggan dari Januari hingga Juli 2025 dengan 753 transaksi yang valid setelah proses pembersihan, serta data eksternal dari Instagram Insight untuk membandingkan profil audiens. Proses penelitian mencakup pengumpulan data, pembersihan, rekayasa fitur menggunakan Python, segmentasi dengan K-Means, dan visualisasi hasil melalui Google Looker Studio. Analisis mengindikasikan adanya empat segmen utama: High Value (17,4%) yang didominasi oleh perempuan berusia 35-44 tahun dari Jakarta; Medium-High Value (10,3%) dengan dominasi serupa berasal dari Jakarta dan Semarang; Medium-Low Value (42,8%) yang merupakan segmen terbesar dengan sebagian besar perempuan berusia 25-44 tahun dari Jakarta; dan Low Value (29,7%) yang juga didominasi oleh perempuan berusia 25-34 tahun dari Jakarta. Visualisasi dasbor Business Intelligence memberikan pemahaman mendalam terkait demografi, geografi, perilaku, dan nilai pelanggan. Temuan penelitian ini menekankan bahwa penggabungan K-Means dengan Business Intelligence tidak hanya meningkatkan ketepatan segmentasi pelanggan, tetapi juga menyediakan dasar strategis dalam merancang promosi digital yang responsif, efektif, dan berkelanjutan di dunia perhotelan.
PELATIHAN PENGGUNAAN MEDIA PEMBELAJARAN BERBASIS VIDEO MENGGUNAKAN APLIKASI FILMORA BAGI GURU SMKS SYAFI’I AKROM KOTA PEKALONGAN Paminto Agung Christianto; Eko Budi Susanto; Mohammad Reza Maulana
BESTIKOM Jurnal Pengabdian Kepada Masyarakat Vol 1 No 2 (2025)
Publisher : Bestikom: Jurnal Pengabdian Kepada Masyarakat

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

Abstract

Penggunaan media pembelajaran yang inovatif menjadi kunci keberhasilan dalam meningkatkan efektivitas pembelajaran di era digital saat ini. Salah satu media yang semakin populer adalah video pembelajaran. Penelitian ini bertujuan untuk memberikan pelatihan penggunaan media pembelajaran berbasis video menggunakan aplikasi Filmora kepada para guru di SMK Syafi’i Akrom, Kota Pekalongan. Metode pelatihan dilakukan melalui workshop intensif dan interaktif yang melibatkan para guru dalam praktik langsung pembuatan dan pengeditan video pembelajaran. Evaluasi dilakukan melalui pre-test dan post-test untuk mengukur peningkatan pengetahuan dan keterampilan guru dalam menggunakan aplikasi Filmora. Selain itu, dilakukan juga survei kepuasan untuk menilai keefektifan pelatihan ini dalam memenuhi kebutuhan dan harapan para guru. Hasil penelitian menunjukkan adanya peningkatan signifikan dalam pengetahuan dan keterampilan guru dalam menggunakan aplikasi Filmora untuk pembuatan video pembelajaran. Survei kepuasan juga menunjukkan bahwa pelatihan ini memberikan dampak positif dan signifikan bagi para guru, dengan mayoritas responden menyatakan kepuasan dan keinginan untuk menerapkan keterampilan yang mereka pelajari dalam pembelajaran di kelas. Pelatihan ini diharapkan dapat menjadi landasan bagi peningkatan kualitas pembelajaran di SMK Syafi’i Akrom, Kota Pekalongan, serta memberikan kontribusi dalam pengembangan strategi pembelajaran berbasis media di institusi pendidikan yang lebih luas.
EVALUASI HASIL KLASTER PADA DATASET IRIS, SOYBEAN-SMALL, WINE MENGGUNAKAN ALGORITMA FUZZY C-MEANS DAN K-MEANS++ Susanto, Eko Budi
Jurnal Surya Informatika Vol. 2 No. 1 (2016): Jurnal Surya Informatika, Vol . 2, No. 1, Mei 2016
Publisher : Universitas Muhammadiyah Pekajangan Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48144/suryainformatika.v2i1.306

Abstract

One technique in data mining is clustering. There are two types of clustering algorithms, namely soft and hard clustering clustering. K-Means++ is hard clustering algorithm which is an improvement of the algorithm K-Means. In K-Means++, center point selection is determined by the concept of probability do not choose at random like at the algorithm K-Means. Algorithm Fuzzy C-Means (FCM) is one of the improvements of the algorithm K-Means. FCM is soft clustering algorithm which applies fuzzy approach to determine the clusters based on the degree of membership. In this study will be evaluated on a cluster results of FCM algorithm and K-Means ++ at the dataset Iris, Wine and Soybean-Small. Results cluster of both algorithms will be compared and will look for the best. The test results of the cluster using the Confusion Matrix and Silhouette Coefficient. The result shows that the algorithm FCM and K-Means have almost similar performance. At the dataset Soybean-Small, Wine both algorithms have the same Silhoutte Coefficient, K-Means++ algorithm has an accuracy rate superior to the FCM algorithm
Transformasi Pemasaran Usaha Konvensional Menuju Digital Melalui Pelatihan di Kelurahan Medono, Pekalongan Paminto Agung Christianto; Eko Budi Susanto
Dedikasi Nusantara: Jurnal Pengabdian Masyarakat Vol. 1 No. 1 (2025): Pemberdayaan Masyarakat dalam UMKM, Digital Marketing, Pelatihan Keterampilan d
Publisher : IndoCompt Publisher

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

Abstract

Pemasaran digital menjadi krusial dalam menghadapi keterbatasan promosi konvensional. Warga Kelurahan Medono, Pekalongan Barat, yang mayoritas merupakan pengusaha batik dan tenun, masih mengandalkan brosur dan spanduk dalam mempromosikan produk mereka. Keterbatasan jangkauan dan konten menjadi kendala utama. Kegiatan pengabdian masyarakat ini bertujuan mengatasi permasalahan tersebut melalui pelatihan digital marketing bagi 20 pelaku usaha. Metode pelatihan yang digunakan adalah praktik langsung pembuatan konten digital marketing, disertai pendampingan intensif. Modul tutorial pembuatan konten menggunakan aplikasi Canva diberikan kepada peserta. Hasil kegiatan menunjukkan bahwa peserta mampu membuat media promosi digital menggunakan Canva untuk mendukung usaha mereka. Keterlibatan aktif masyarakat dalam kegiatan ini menunjukkan antusiasme tinggi untuk mengadopsi teknologi digital demi pengembangan usaha, sekaligus memperkuat kolaborasi antara akademisi dan komunitas lokal.
PENGEMBANGAN KEMAMPUAN UMKM DALAM MEMPRODUKSI KONTEN VIDEO MELALUI CAPCUT SEBAGAI STRATEGI PEMASARAN DIGITAL DI MEDIA SOSIAL Eko Budi Susanto; Paminto Agung Christianto; Wachid Darmawan
Servirisma Vol. 5 No. 1 (2025): Servirisma : Jurnal Pengabdian kepada Masyarakat
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Kristen Duta Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21460/servirisma.2025.51.92

Abstract

This Community Service (PkM) activity was carried out with the aim of improving the skills of Micro, Small, and Medium Enterprises (MSMEs) in Setono Village in producing and editing promotional videos using the CapCut application. In today's digital era, the ability to create interesting video content is an important asset for MSMEs in marketing their products. This workshop managed to attract 40 participants, exceeding the initial target of 30 participants, which shows the high enthusiasm of MSMEs to improve their digital skills. The materials presented include the basics of creating video content, effective shooting techniques, introduction and tutorials on using the CapCut application, and product branding strategies to increase sales. The results of the post-test and direct practice showed that participants had understood and were able to apply video editing skills using CapCut. Although face-to-face discussion sessions were limited, interaction and consultation continued through personal communication. This activity is expected to make a significant contribution to increasing the competitiveness of MSMEs in Setono Village through the use of social media as a means of promotion. The success of this program was supported by the enthusiasm of the participants and the relevance of the material to the needs of MSMEs in the digital era.
Co-Authors Agung Christianto , Paminto Anas Syaifudin Anzila, Anin Naba Arochman Bambang Ismanto Budijanto, Hari Agung Cheryl Louisa Loedwyca Christianto, P. A. Christianto, Paminto Agung Devi Sugianti Devi Sugianti Edi Purwanto Edi Purwanto Faizal Kurniawan Hari Agung Budijanto Ilyas, Agus Irwan Sembriring Iwan Kresna Setiadi Janah, Roikhatul Kurniawan, Ichwan M. Kemal Aditya Hananta M. Reza Maulana Maulana, M. Reza Maulana, Moh. Reza Mohammad Reza Maulana Mohammad Reza Maulana, Mohammad Reza Muhammad Septiyanto Mulyono, Santosa Imam Nur Fadhilah P. A. Christianto Paminto Agung Christianto Paminto Agung Christianto Paminto Agung Christianto Paminto Agung Christianto Paminto Agung Christianto Restyandito Reza Maulana, Mohammad Rizqi Wijonarko Rony, Zahara Tussoleha Sari, Zada Amalia Agatha Sattriedi Sattriedi Sattriedi Wahyu Binabar Sattriedi Wahyu Binabar Slamet Joko Prasetiono Suharyadi Syaifudin, Anas Tory Ariyanto Tory Ariyanto Tri Agus Setiawan Wachid Darmawan Wahyu Binabar, Sattriedi Wijonarko, Rizqi