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All Journal Telematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat JOURNAL OF APPLIED INFORMATICS AND COMPUTING JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Abdi Insani Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Indonesia Jurnal Elektrosista The Indonesian Journal of Computer Science
Bambang Pilu Hartato
Universitas Amikom Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences

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Synonym Measurement Through Semantic Similarity Using the SOC-PMI Method Uswatun Hasanah; Bambang Pilu Hartato; Mitra Yulianti; Saeful Haq Faruqi
Telematika Vol 13, No 1: Februari (2020)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v13i1.941

Abstract

Abstract: Measurement of synonyms can be an important task in measuring word similarity. This work cannot be done syntactically, but must dig deeper about its semantics. Semantic relations can be anything, such as synonyms, antonyms, hyponymy, homonymy and polysemy. This research works on finding synonym values using the Second Order Co-occurrence Pointwise Mutual Information (SOC-PMI) method. The data used are 30 questions on the TOEFL exam. Each question consists of one word as a question and four reference answers as alternative answers. The results show very low accuracy (30%) since there are only 9 out of 30 answers that actually show the synonym. In addition, the LCS method was also tested to get a character-based similarity score. LCS method is able to achieve a higher similarity score of 43.33%. Finally, the idea of hybrid method by combining character-based and semantic-based methods can be considered in longer words to produce a fairer similarity score.Abstrak: Pengukuran sinonim dapat menjadi pekerjaan yang penting dalam mengukur kemiripan kata. Pekerjaan ini tidak dapat dilakukan secara sintaksis, tetapi harus dilakukan dengan menggali lebih dalam tentang semantiknya. Hubungan semantik dapat berupa apa saja, seperti sinonim, antonim, hiponim, homonim, dan polisemi. Penelitian ini berusaha untuk menemukan nilai-nilai sinonim menggunakan metode Second Order Co-occurrence Pointwise Mutual Information (SOC-PMI). Data yang digunakan adalah 30 pertanyaan pada ujian TOEFL. Setiap pertanyaan terdiri dari satu kata sebagai pertanyaan dan empat jawaban referensi sebagai jawaban alternatif. Hasil menunjukkan nilai akurasi yang sangat rendah (30%) karena hanya ada 9 dari 30 jawaban yang benar-benar menunjukkan sinonim. Selain itu, metode LCS juga diuji untuk mendapatkan skor kemiripan berdasarkan karakternya. Metode LCS mampu mencapai skor kemiripan yang lebih tinggi yaitu 43,33%. Akhirnya, gagasan metode hybrid dengan menggabungkan metode berbasis karakter dan metode berbasis semantik semantik dapat dipertimbangkan untuk kata-kata yang lebih panjang agar menghasilkan skor kesamaan yang lebih adil.
Penerapan Convolutional Neural Network pada Citra Rontgen Paru-Paru untuk Deteksi SARS-CoV-2 Bambang Pilu Hartato
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 4 (2021): Agustus 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (835.437 KB) | DOI: 10.29207/resti.v5i4.3153

Abstract

COVID-19 was officially declared as a pandemic by the WHO on March 11, 2020. For COVID-19, the testing methods commonly used are the Antibody Testing and RT-PCR Testing. Both methods are considered to be the most effective in determining whether a person has been suffered from COVID-19 or not. However, alternative testing methods need to be tried. One of them is using the Convolutional Neural Network. This study aims to measure the performance of CNN in classifying x-ray image of a person’s chest to determine whether the person is suffered from COVID-19 or not. The CNN model that was built consists of 1 convolutional 2D layer, 2 activation layers, 1 maxpooling layer, 1 dropout layer, 1 flatten layer, and 1 dense layer. Meanwhile, the chest x-ray image dataset used is the COVID-19 Radiography Database. This dataset consists of 3 classes, i.e. COVID-19 class, NORMAL class, and VIRAL_PNEUMONIA. The experiments consisted of 4 scenarios and were carried out using Google Colab. Based on the experiments, the CNN model can achieve an accuracy of 98.69%, a sensitivity of 97.71%, and a specificity of 98.90%. Thus, CNN has a very good performance to classify the disease based on a person’s chest x-ray.
Pelatihan Teknis Pengaduan Online Kekerasan Terhadap Perempuan dan Anak Untuk Ibu-Ibu PKK Kelurahan Purwosari Baturraden Fiby Nur Afiana; Bambang Pilu Hartato
Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Vol 1, No 3 (2018): Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (370.114 KB) | DOI: 10.31294/jabdimas.v1i3.4153

Abstract

AbstrakBerita mengenai korban tindak kekerasan di kalangan perempuan dan anak memang belum terlalu banyak ditemukan di dalam berbagai literatur yang ada. Hal tersebut bisa saja disebabkan oleh persepsi masyarakat bahwa kekerasan yang dialami merupakan aib dan lebih baik disembunyikan. Selain itu, terkadang masyarakat, khususnya warga Kelurahan Purwosari Baturraden, juga kurang begitu paham mengenai tindakan-tindakan yang dikategorikan kekerasan dan prosedur yang tepat untuk melaporkan tindak kekerasan yang terjadi. Di sisi lain, Komisi Perlindungan Anak Indonesia (KPAI) dan Komisi Nasional Anti Kekerasan terhadap Perempuan (Komnas Perempuan) telah mencoba beberapa cara, bahkan penggunaan teknologi, untuk menjangkau masyarakat agar berperan aktif dalam mencegah dan menagani tindak kekerasan terhadap anak dan perempuan yang ada di sekitarnya. Melalui pengabdian ini, tim dosen STMIK Amikom Purwokerto berusaha memberikan penyuluhan dan pendampingan kepada warga Kelurahan Purwosari Baturraden, khususnya ibu-ibu PKK, untuk berperan aktif dalam mengenali dan mencegah tindak kekerasan terhadap perempuan dan anak yang ada di sekitarnya. Berdasarkan hasil evaluasi yang dilakukan, lebih dari 90% masyarakat sasaran mulai sadar dan dapat mengidentifikasi tindak kekerasan serta mengetahui bagaimana cara untuk melakukan pengaduan melalui layanan yang disediakan KPAI dan Komnas Perempuan setelah mengikuti penyuluhan yang telah dilakukan. Sehingga pengabdian ini dinilai telah berhasil mencapai tujuannya untuk mengedukasi masyarakat sasaran mengenai kekerasan terhadap anak dan perempuan.Kata Kunci : Anak-anak, Kekerasan, Perempuan
Pengenalan SIERAS pada Civitas Academica Program Studi Teknologi Informasi Universitas Amikom Purwokerto Bambang Pilu Hartato; Agung Prakoso; Hasri Akbar Awal Rozaq
Jurnal Pengabdian Masyarakat Indonesia Vol 1 No 3 (2021): JPMI - Juni 2021
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpmi.14

Abstract

Jumlah kasus COVID-19 belum menunjukkan trend penurunan. Hal tersebut mempengaruhi sektor pendidikan. Salah satu kebijakan yang diambil untuk menanggulangi dampak tersebut adalah diadakannya pembelajaran daring. Kebijakan tersebut memberikan tantangan tersendiri bagi civitas academica, tak terkecuali bagi Prodi Teknologi Informasi, Universitas Amikom Purwokerto. Salah satu perubahan yang terjadi pada proses pembelajaran yang dilakukan adalah kegiatan ujian yang harus dilakukan secara daring. Hal ini membuat pengelola prodi mencoba menggunakan beberapa teknologi untuk memenuhi kebutuhan tersebut, seperti E-Learning bernama ILIAS dan Google Form. Namun, keduanya masih memberikan kesulitan tersendiri dalam melakukan tata kelola ujian, termasuk proses penilaian yang harus dilakukan oleh dosen. Melalui kegiatan pengabdian ini, kami mencoba memperkenalkan sistem bernama SIERAS sebagai salah satu alternatif sistem tata kelola ujian. Kegiatan diikuti oleh 5 Dosen dan 20 mahasiswa. 60% dosen menyatakan “sangat terbantu” sementara sisanya menyatakan “terbantu”. Dari sisi mahasiswa, 80%-nya menyatakan SIERAS “mudah digunakan”, 15% menyatakan “sangat mudah digunakan”, dan sisanya menyatakan “cukup mudah digunakan”. Dengan demikian, kegiatan pengabdian ini dinyatakan efektif dan memberikan dampak positif bagi peserta.
Pendampingan Penggunaan Aplikasi Turnitin dan Mendeley untuk Peningkatan Kualitas Publikasi Ilmiah Chyntia Raras Ajeng Widiawati; Bambang Pilu Hartato; Suliswaningsih Suliswaningsih; Retno Waluyo; Danty Kusumaningtyas
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 5, No 3 (2022): September 2022
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/ja.v5i3.740

Abstract

Penulisan artikel atau publikasi ilmiah merupakan salah satu hal penting yang harus diperhatikan khususnya oleh para tenaga pendidik seperti Dosen. Beberapa masalah yang kerap dihadapi oleh Dosen diantaranya adalah masalah orisinalitas serta penyusunan referensi. Hal tersebut masih sering terjadi di lingkungan akademik karena kurangnya penguasaan dalam memanfaatkan tools yang sebenarnya akan sangat membantu meninimalisir permasalahan serupa. Sehingga perlu adanya pendampingan untuk menyelesaikan beberapa permasalahan tersebut salah satunya adalah pendampingan teknis penggunaan Aplikasi Turnitin dan Mendeley. Pendampingan ini akan diberikan kepada 2 orang staff perwakilan dari masing-masing Fakultas, serta beberapa Dosen yang ada di lingkungan Universitas Muhammadiyah Purwokerto. Pelaksanaan kegiatan pelatihan Turnitin dan Mendeley sangat membantu bagi para Dosen dan Operator ditiap Fakultas pada lingkungan Universitas Muhammadiyah Purwokerto.
PENGEMBANGAN PROTOTIPE ALAT KEAMANAN BARANG ELEKTRONIK BERBASIS INTERNET OF THINGS (IOT) Nur Athif Oldika Gunawan; Nila Feby Puspitasari; Bambang Pilu Hartato
JURNAL ELEKTROSISTA Vol. 12 No. 1 (2024): DESEMBER 2024
Publisher : PPM Sdirjianbang Akademi Militer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63824/jtep.v12i1.242

Abstract

The increasing security risks today have driven the development of more sophisticated protection solutions, especially for personal electronic goods. This study aims to design and implement an innovative Internet of Things (IoT)-based security device, designed to protect electronic goods such as mobile phones, laptops, and other electronic devices. The technology used includes the integration of smart sensors, such as GPS, Ultrasonic, and Passive Infrared, which function to detect threats accurately and responsively. This prototype-based system is equipped with smart sensors designed to detect potential threats to personal goods. In addition, a user application was successfully developed to support efficient interaction between users and the system. The test results show that this system is portable and capable of providing real-time notifications to users regarding threats to electronic goods, as well as responding to these threats quickly and appropriately. The approach taken not only emphasizes physical security, but also integrates modern technology to provide an efficient, practical, and easily accessible monitoring experience. This study offers an innovative solution in protecting personal electronic goods in the digital era.
Machine Learning-Based Approach for HIV/AIDS Prediction: Feature Selection and Data Balancing Strategy Rahim, Abdul Mizwar A; Ridwan, Ahmad; Hartato, Bambang Pilu; Asharudin, Firman
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9125

Abstract

HIV/AIDS remains a significant global health challenge, requiring accurate predictive models for early detection and improved clinical decision-making. However, developing an effective predictive model faces challenges such as data imbalance and the presence of irrelevant features, which can compromise model accuracy. This study aims to enhance the performance of AIDS infection prediction models by integrating feature selection, data balancing, and machine learning classification techniques. Feature selection is conducted using Pearson Correlation, Mutual Information, and Chi-Square tests to retain only the most relevant features. Random Oversampling, SMOTE, and ADASYN are employed to address data imbalance and improve model robustness. Nine machine learning algorithms, including Decision Tree, Random Forest, XGBoost, LightGBM, Gradient Boosting, Support Vector Machine, AdaBoost, and Logistic Regression, are tested for classification. Performance evaluation using confusion matrix, precision, recall, F1-score, and AUC-ROC shows that tree-based models (Random Forest, Extra Trees, and XGBoost) achieve the best results, particularly in handling minority class predictions. The study concludes that combining feature selection, data balancing, and machine learning techniques significantly improves predictive performance, making it a valuable approach for early detection and clinical decision support in HIV/AIDS diagnosis. Future research may explore hyperparameter tuning and real-world clinical data integration to enhance practical applicability.
Pengujian YOLOv8 dan Centroid Tracking pada Sistem Deteksi, Klasifikasi, dan Penghitungan Jumlah Kendaraan Dharmasaputra, Kevin Dicky; Hartato, Bambang Pilu
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10873

Abstract

An automatic vehicle detection and counting system is essential for Intelligent Transportation Systems (ITS) to monitor and manage traffic effectively. This study evaluates the performance of the lightweight YOLOv8n (nano) model for vehicle detection and classification, combined with a Centroid Tracking algorithm to improve vehicle counting accuracy. YOLOv8n was selected for its balance between computational efficiency and detection accuracy, making it suitable for devices with limited resources. The research involved collecting a dataset of seven vehicle classes (bus_l, bus_s, car, truck_l, truck_m, truck_s, truck_xl), followed by data preprocessing and training the YOLOv8n model for 40 epochs. Data augmentation techniques were applied to enhance data variability and improve model robustness. The Centroid Tracking algorithm was integrated to maintain vehicle identity across frames and prevent double counting. Model evaluation used precision, recall, F1-score, and mean Average Precision (mAP). Results show YOLOv8n achieved an overall mAP@0.5 of 0.820. The “car” class attained the highest mAP of 0.963, while “truck_s” had the lowest at 0.665, mainly due to imbalanced data distribution. The Centroid Tracking effectively maintained object identities and provided consistent vehicle counts during testing. This combination offers a reliable and efficient system suitable for real-time traffic monitoring, parking management, and enhancing road safety. The YOLOv8n and Centroid Tracking-based system demonstrates strong potential for practical ITS applications, especially on devices with limited computational resources. Future work should focus on expanding the dataset and improving class balance to further enhance detection accuracy and system robustness.
Evaluating the Impact of Random Over Sampling on IndoBERT Performance for Indonesian Sentiment Analysis Alfinsyah, Dimas Ramadhan; Hartato, Bambang Pilu
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11488

Abstract

Sentiment analysis is a prominent research area in natural language processing (NLP). For the Indonesian language, IndoBERT has emerged as a leading model due to its competitive performance. However, its effectiveness is strongly influenced by balanced class distribution. A common challenge arises because user reviews on digital platforms, such as the Google Play Store, often exhibit imbalanced classes. This study investigates the effectiveness of the Random Over Sampler (ROS) technique in improving IndoBERT’s performance under imbalanced data conditions. The dataset consists of 13,821 user reviews of the IDN App collected from the Google Play Store between 2015 and July 2025. Prior to modeling, data preprocessing was performed, including punctuation removal, case folding, stopword removal, tokenizing, normalization, and stemming to ensure textual consistency. Reviews were categorized into two sentiment classes: positive (3–5 stars) and negative (1–2 stars). Two experimental scenarios were conducted: (1) IndoBERT without ROS and (2) IndoBERT with a balanced dataset using ROS. Model performance was evaluated using accuracy, precision, recall, and F1-score, with data split into 70% training, 20% validation, and 10% testing. Results showed a significant improvement after ROS implementation: 94.55% accuracy, 94.61% precision, 94.53% recall, and 94.54% F1-score. Confusion matrix analysis indicated improved classification of the minority class, reducing the error rate by 49%. However, learning curve analysis revealed potential overfitting due to ROS. Further research is needed to optimize ROS strategies for better performance and generalization.
PENINGKATAN KUALITAS DAN DAYA SAING PRODUK GULA NIRA MELALUI TEKNOLOGI PANGAN DAN STRATEGI REBRANDING PADA KELOMPOK TANI SERBA USAHA, DESA HARGOROJO, PURWOREJO Bimantoro, Wajar; Riwanto, Yudha; Hartato, Bambang Pilu
Jurnal Abdi Insani Vol 13 No 1 (2026): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v13i1.3492

Abstract

This community service activity was motivated by the low quality and competitiveness of palm sap sugar products produced by the Serba Usaha Farmer Group in Hargorojo Village, Purworejo. The main issues included unhygienic traditional production methods, unstable moisture content, and unattractive packaging and branding. The objective of this program was to improve the quality and competitiveness of palm sap sugar through the application of food technology using a sugar drying machine and an integrated rebranding strategy. The methods involved field observation, training on the use of the drying machine to achieve standardized moisture levels, mentoring in packaging and label design, and evaluation of implementation results. The outcomes showed a significant improvement in product quality, characterized by drier texture, more uniform color, and longer shelf life. Furthermore, the farmer group gained a better understanding of the importance of process standardization and visual branding in enhancing market appeal. The rebranded product received positive feedback from local consumers and began to be distributed to regional souvenir shops. In conclusion, the application of a sugar drying machine as a simple food technology combined with rebranding strategies effectively improved the quality, added value, and competitiveness of palm sap sugar products.
Co-Authors Abdul Mizwar A. Rahim Agung Prakoso Ahmad Ridwan Alfinsyah, Dimas Ramadhan Asharudin, Firman Chyntia Raras Ajeng Widiawati Danang Wijayanto Danty Kusumaningtyas Dharmasaputra, Kevin Dicky Fiby Nur Afiana Hasri Akbar Awal Rozaq Mitra Yulianti Nila Feby Puspitasari Nur Athif Oldika Gunawan Retno Waluyo Riwanto, Yudha Saeful Haq Faruqi Suliswaningsih Uswatun Hasanah Wajar Bimantoro, Wajar Wicaksono, Aryo Bimo