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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal F. Teknik : RESULTAN Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknik Komputer AMIK BSI Cakrawala : Jurnal Humaniora Bina Sarana Informatika Paradigma Jurnal Ilmiah FIFO Bina Insani ICT Journal Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System Jurnal Mahasiswa Bina Insani Informatics for Educators and Professional : Journal of Informatics Information Management For Educators And Professionals (IMBI) Jurnal Teknik Informatika STMIK Antar Bangsa Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Komtika (Komputasi dan Informatika) IKRA-ITH EKONOMIKA Jurnal ICT : Information Communication & Technology JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Kajian Ilmiah Jurnal Sistem Informasi Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Sains Teknologi dalam Pemberdayaan Masyarakat Journal of Students‘ Research in Computer Science (JSRCS) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Journal of Computer Science Contributions (Jucosco) Jurnal Komtika (Komputasi dan Informatika)
Herlawati Herlawati
Fakultas Ilmu Komputer, Universitas Bhayangkara Jakarta Raya
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Social Sciences Other

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Pendeteksian dan Klasifikasi Sampah pada Bank Sampah Berbasis Web Menggunakan YOLOv11 Nabila , Marsyanda Salsa; Hidayat, Agus; Herlawati, Herlawati
Journal of Students‘ Research in Computer Science Vol. 6 No. 1 (2025): Mei 2025
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/r5me0z35

Abstract

The problem of poorly managed household waste management can increase the burden on the environment and reduce the effectiveness of recycling. Waste banks in general still rely on manual systems in sorting waste which is prone to errors and requires more labor. This research aims to develop a web-based waste detection and classification system using the You Only Look Once (YOLO) version 11 yolov11n (nano) method. The research method included downloading the main secondary dataset named R1 Test version 15 from the Roboflow Universe platform, collecting other secondary datasets from internet scraping and manual photography, which resulted in a total of 27,400 images of trash with nine different types, namely bottle, cans, cardboard, cup, foil, food, paper, paper_bag, and plastic.The results show that the yolov11n model is able to detect objects with sufficient accuracy and light computational resources by producing a precision value of 91,7%, recall of 89%, mAP50 of 93,2% and mAP50-95 of 75,8% in all classes. The best model results obtained are integrated into the web using the flask framework.
Analisis Sentimen Masyarakat Terhadap PHK di Indonesia Pada Twitter Menggunakan Naïve Bayes dan Support Vector Machine (SVM) AlHakim, Abdu Malik; Atika, Prima Dina; Herlawati, Herlawati
Journal of Students‘ Research in Computer Science Vol. 6 No. 1 (2025): Mei 2025
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/96sfw544

Abstract

The phenomenon of layoffs in Indonesia has led to various public opinions, especially on social media. This research aims to analyze public sentiment on the layoff issue using data from Twitter, and compare the performance of two text classification algorithms, namely Naïve Bayes and Support Vector Machine. The Knowledge Discovery in Databases approach is used as the research framework, which includes the stages of data selection, text cleaning, transformation, classification, and evaluation. A total of 3,458 tweets were collected and processed through the pre-processing stage, then classified into positive and negative sentiments. Performance assessment was conducted with three scenarios of training and test data sharing: 80:20, 70:30, and 90:10. The results showed that Support Vector Machine gave the highest accuracy of 84.93% in the 90:10 scenario, compared to Naïve Bayes with 82.61% accuracy in the same scenario. Visualization through wordcloud was also used to strengthen the interpretation of dominant words in public opinion. The findings show that classification algorithms can be utilized to understand public perceptions of employment issues and support social data-based decision-making. This research can be further developed by expanding data coverage and evaluating more complex methods to improve classification accuracy.
Comparative Study of PCA, t-SNE, and UMAP for CNN Feature Representation of Image Classification Herlawati, Herlawati; Handayanto, Rahmadya Trias
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11634

Abstract

Currently, the use of Deep Learning is widespread across various domains, with Convolutional Neural Networks (CNNs) as one of its main pioneers due to the principle of convolution. Recent methods continue to emerge with steadily increasing accuracy, in some cases approaching perfection. However, their implementation is often limited by the lack of sufficient computational resources in many environments. Moreover, the growing demand for explainable AI compels researchers to explore approaches that reveal the inner workings of deep learning models rather than treating them as mere black boxes. In this study, a simple CNN model is employed as a testbed for examining the feature extraction process through convolution, which is subsequently transformed into a user-friendly two-dimensional representation. The dataset used in this study is the Cats and Dogs dataset from Kaggle, which contains 25,000 labeled images equally distributed between the two classes. The dimensionality reduction methods utilized include Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). The results demonstrate that UMAP achieves superior performance compared to PCA and t-SNE, with the highest silhouette score and a lower Davies–Bouldin index, indicating more compact and well-separated feature clusters.
Klasifikasi Sentimen Opini Metaverse dari Twitter Menggunakan Algoritma Support Vector Machine Herlawati, Herlawati; Muhajirin, Adi; Izdihar, Zalfa
Jurnal Ilmiah FIFO Vol 15, No 1 (2023)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2023.v15i1.007

Abstract

With the increasing use of Twitter, a real-time social media platform, it has become one of the places or spaces for people to express their opinions about the metaverse. Therefore, the development of a program capable of classifying tweets based on their opinions into positive, negative, and neutral categories is necessary. In conducting sentiment analysis, the Support Vector Machine (SVM) algorithm is used for classification. The results of this research, through testing using a confusion matrix, yield an accuracy rate of 0.83 or 83%, indicating the level of agreement between the model's predictions and the actual outcomes. Additionally, a precision of 0.93 or 93% is obtained, which shows the model's ability to accurately identify positive, negative, and neutral sentiments in tweets, and a recall of 0.83 or 83%, which describes the model's capability to find and classify accurately.
Pelatihan Mentimeter Sebagai Media Interaksi Dalam Pembelajaran Daring Pada SMAN 14 Bekasi Herlawati; Nidaul Khasanah, Fata; Sari, Rafika
Journal Of Computer Science Contributions (JUCOSCO) Vol. 1 No. 1 (2021): Januari 2021
Publisher : Lembaga Penelitian, Pengabdian kepada Masyarakat dan Publikasi Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/jucosco.v1i1.454

Abstract

Guru sebagai fasilitator bukan hanya sebagai pendidik dan pengajar saja pada abad 21 ini. Guru dituntut mempunyai kemampuan lebih dalam kegiatan pembelajaran. Guru harus mampu memadukan pembelajaran, konteks yang bermakna dan harus memberikan kesempatan kepada siswa untuk dapat mengakses materi pembelajaran setiap saat dan memberikan penilaian formatif yang adil, salah satunya dengan menggunakan konsep gamifikasi. Terlebih di masa pandemi seperti sekarang ini, perlu adanya kegiatan pembelajaran interaksi yang baik antara guru dan siswa dalam kegiatan pembelajaran secara daring. Salah satunya menggunakan aplikasi Mentimeter, yang merupakan aplikasi berbasis website yang dimanfaatkan untuk melakukan kegiatan survey dalam suatu kegiatan seminar atau pembelajaran. Metode pelaksanaan dari kegiatan pengabdian kepada masyarakat dimulai dari koordinasi dengan pihak mitra untuk mendiskusikan tentang banyaknya peserta, dan tanggal pelaksanaan, dilanjutkan dengan kegiatan pelatihan menggunakan aplikasi daring dan bagian akhir melakukan koordinasi dengan mitra terkait pelaporan dari kegiatan pengabdian ini. Mitra dari kegiatan ini adalah SMA Negeri 14 Kota Bekasi. Hasil yang diharapkan dari kegiatan ini pihak mitra dapat memahami mengenai aplikasi Mentimeter dan mengetahui bagaimana membuat media interaktif dalam proses pembelajaran daring dengan menggunakan aplikasi Mentimeter sebagai upaya mewujudkan program pemerintah merdeka belajar.
Pelatihan Pemanfaatan Software Pendukung Statistik Dalam Pengolahan Data Kuantitatif Bagi Guru-Guru SMA Herlawati, Herlawati; Atika, Prima Dina; Handayanto, Rahmadya Trias; Sumadyo, Malikus; Samsiana, Seta; Gunarti, Anita Setyowati Srie; Maimunah, Maimunah
Journal Of Computer Science Contributions (JUCOSCO) Vol. 2 No. 2 (2022): Juli 2022
Publisher : Lembaga Penelitian, Pengabdian kepada Masyarakat dan Publikasi Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/79gqt343

Abstract

Teachers need additional knowledge of quantitative data processing using statistical support software because of the diverse educational backgrounds of teachers such as education science, management science, although there are also those whose educational background is mathematics and natural sciences (MIPA). In addition, the level of awareness of teachers to study statistics for processing quantitative data is still low and is considered less important. Solutions that can be given to this community service activity include organizing training on the use of statistical support software to provide understanding and skills in the use of such software, including Microsoft Ms.Excel and Matlab Mobile in an effective and efficient manner. This can be done through the mentoring process in this activity and will be followed by other advanced trainings. The results of this training, based on a survey using an online mentimeter, showed that it was very useful and the participants wanted to continue this training with the theme of using the Statistical Package for the Social Sciences (SPSS).
Pelatihan Pemanfaatan Software Pendukung Dalam Pembuatan Artikel Ilmiah Terpublikasi Bagi Guru-Guru SMA Herlawati, Herlawati; Atika, Prima Dina; Hendharsetiawan, Andy Achmad; Handayanto, Rahmadya Trias; Sumadyo, Malikus; Whidhiasih, Retno Nugroho; Ekawati, Inna; Irwan, Dadan; Haryono, Haryono
Journal Of Computer Science Contributions (JUCOSCO) Vol. 3 No. 2 (2023): Juli 2023
Publisher : Lembaga Penelitian, Pengabdian kepada Masyarakat dan Publikasi Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/sgfq4k95

Abstract

Supportive software for scientific article creation is an application that assists in writing scientific articlesefficiently and effectively. This software features reference management, note-taking, text review, andformatting according to academic standards. The training to be provided will focus on creating scientificarticles using supportive software to enhance the competence of teachers in publishing their scientific articles.By understanding how to create scientific articles, high school teachers can improve their academic abilities,serve as positive examples for students, provide resources, and contribute to research and educationaldevelopment. This can help improve the quality of education and produce competent and skilled students. Theproposed solution is to organize training on the utilization of supportive software in scientific article creation.This training will provide understanding and skills in using software such as Mendeley and ChatGPT. Mendeleyis a reference management software that assists in collecting, managing, and storing references for scientificarticles. Additionally, Mendeley helps organize references according to various writing styles such as APA,MLA, Chicago, Vancouver, and IEEE. ChatGPT, on the other hand, is a natural language model that helpsgenerate structured and meaningful texts. In the context of scientific article creation, ChatGPT can assist informulating and organizing ideas or concepts, as well as providing suggestions or feedback for scientificwriting. Both of these software options can be chosen based on the needs and preferences of the writer. Theresults of this training, based on a survey using an online mentimeter, showed that it was very useful and theparticipants wanted to continue this training.
Land Cover Segmentation of Multispectral Images Using U-Net and DeeplabV3+ Architecture Herlawati; Handayanto, Rahmadya Trias
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 1 (2024): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v17i1.1206

Abstract

The application of Deep Learning has now extended to various fields, including land cover classification. Land cover classification is highly beneficial for urban planning. However, the current methods heavily rely on statistical-based applications, and generating land cover classifications requires advanced skills due to their manual nature. It takes several hours to produce a classification for a province-level area. Therefore, this research proposes the application of semantic segmentation using Deep Learning techniques, specifically U-Net and DeepLabV3+, to achieve fast land cover segmentation. This research utilizes two scenarios, namely scenario 1 with three land classes, including urban, vegetation, and water, and scenario 2 with five land classes, including agriculture, wetland, urban, forest, and water. Experimental results demonstrate that DeepLabV3+ outperforms U-Net in terms of both speed and accuracy. As a test case, Landsat satellite images were used for the Karawang and Bekasi Regency areas.
Machine Learning Berbasis Desktop dan Web dengan Metode Jaringan Syaraf Tiruan Untuk Sistem Pendukung Keputusan Handayanto, Rahmadya Trias; Herlawati, Herlawati
Jurnal Komtika (Komputasi dan Informatika) Vol 4 No 1 (2020)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v4i1.3698

Abstract

Machine learning application demand is increased massively because it provides good ability in the classification that is needed by decision makers. Machine learning application uses a programming language with strong characteristics in computing, usually the back-end programming language, such as Matlab, Python, R, etc. The obstacle faced by the decision support system developer is preparing an interface that makes it easy for the user. Some back-end programming languages have provided a good interface. Therefore, in this study they were compared by taking the case of a scholarship decision support system. The language used is Python with two web-based applications including Google Interactive Notebook and Flask framework. Both devices have their respective advantages and are worthy of being the first choice in the design of decision support systems.Python has advantages with framework Flask support and Matlab is easy in interface design.
Penentuan Lokasi Lahan dengan Sistem Pendukung Keputusan Kriteria Jamak Berbasis Sistem Informasi Geografis Herlawati, Herlawati; Khasanah, Fata Nidaul
Jurnal Komtika (Komputasi dan Informatika) Vol 4 No 2 (2020)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v4i2.4548

Abstract

Searching for appropriate land for a specific purpose is important, especially in a city. Some studies used multicriteria analysis searching for the best location, such as AHP, ANP, SAW, etc. However, the study usually uses some statistical methods that need more processing for the end-users. This study uses a Geographic Information System (GIS) to implement the multicriteria analysis for searching the proper land use location, especially for business and education in Bekasi City, West Java, Indonesia. Same layers for analysis were created using ArcGIS 10.1, including pollution, road, flood risk, open space/park, lake and recreation area, and land price. The weight-overlay method was used as a multicriteria method. The result showed the three regions for business and education, i.e. high, medium, and low suitability. The conversion from the region into administrative boundary is generated in order easy for local land-use planners to implement their plans.
Co-Authors A.A. Ketut Agung Cahyawan W Abd Rohman Abdul Kholis Achmad Wira Wiguna Adam Adam Adam Fajariansyah Adi Muhajirin Adi Supriyatna admin admin Aera Santiana Agus Hidayat Agustin, Syafira Cessa Ajie Prasetya Ajif Yunizar Pratama Yusuf AlHakim, Abdu Malik Andy Achmad Hendharsetiawan Anggaini, Meri Anisa Feby Yana Anita Setyowati Srie Gunarti Anita Setyowati Srie Gunarti Anita Setyowati Srie Gunarti Anita Setyowati Srie Gunarti Anton Anton Ardiansyah, Muhamad Arrasyid, Rizky Maulana Asmoro Bangun Priambodo Atika , Prima Dina Ayu Afidarisa Rahma Bangga Tua Siregar Bayu Andriansyah Ben Rahman Beno Aditya Sanusi Beno Aditya Sanusi Benrahman Bhagaskara Farhan Wiguna Binu Nuryadi Budi Santoso Bunga Pratiwi Cahyaaty, Tata Arya Christhover , Robbie Dadan Irwan Dani Dani Daniel Jhon Rosinton Hutauruk Desi Puspasari Diah Putri Ramadhani Dicki Rizki Amarullah Didik Setiyadi Dinda Mutiara Hanum Dwi Budi Santoso Dwi Budi Srisulistiowati Eka Puspita Sari Eka Suryani Pratiwi Ekawati, Inna Endang Retnoningsih Erene Gernaria Sihombing, Erene Gernaria Ervan Dwi Kurniawan Fachrullyanta Adi Saputra Fadilah, Naufal Arif Fahrika, Andi Ika Faisal Adi Saputra Fandiansyah, Rafly Fata Nidaul Khasanah Feni Meilan Tasiba Firyal Rosiana Dita Frieyadie Galih Apriansha Pradana Gedhe Hilman Wakhid Gilby Lionska Wenas Gymnastiar, Muhammad Handry Hartino Haris, Syamsul Alam Harviansyah, Muhammad Haryono Haryono Haryono Hendharsetiawan , Andy Achmad Hendharsetiawan, Andy Achmad Heri Prabowo Hero Suhartono Hero Suhartono, Hero Hutauruk , Daniel Jhon Rosinton Icah Fitri Yani Idaul Hasanah Ikhsan Dwikurniawan Ikhsan Dwikurniawan Ira Wardani Irham Cahya Nugraha Irwan Raharja Ivan Nur Firdaus Izdihar, Zalfa Jaja Jaja JAJA, JAJA Joko Dwi Hartanto Juandika Shevani Julaiwa, Siti Hawa Karnita Afnisari, Karnita Krisendo Setiawan Kukuh Dwi Prasetyo Kurniawan, Ervan Dwi Kustanto , Prio Ladyana Suciani Syafitri Lestari, Tyastuti Sri Lubis, Riski Aditya Magdalena, Caroline Julyana Maimunah Maimunah Maimunah Maimunah Maimunah Maimunah, Maimunah Malikus Sumadyo Mardi Yudhi Putra Mayora Lolly Ishimora Merza Dheo Prakoso Muhamad Ardiansyah Muhammad Harviansyah Muhammad Muharrom Muhammad Riky Sudrajat Muhammad Zidan Al Faiq Nabila , Marsyanda Salsa Ningrum, Mirza Cahya Nita Merlina Nita Merlina, Nita Nitin Kumar Tripathi Noer Hikmah Novaldi Nur Pratama Novianto, Krisna Nunung Hidayatun Nur Amanda Pratiwi Nurchayati Nurchayati Nurcholis Nurcholis Oriza Sativa Dinauni Silaen Pahrizal, Pahrizal Popy Purnamasari Wahid Suyitno Pradana , Galih Apriansha Pramuhesti, Salwa Nabiila Priatna , Wowon Prihatin, Sandy Satyo Prima Dina Atika Purnama, Putra Aldi Purnomo, Rakhmat Purwanti, Santi Rachmatin, Nida Rafika Sari RAFIKA SARI Rahmadya Trias Handayanto Raihan Nurfaidzi Ramadhan, Sahara Ramadhani, Diah Putri Rasim Rasim Rasim, Rasim Rejeki , Sri Retno Nugroho Whidhiasih Retno Sari Riska Utami Dewi Rismayana, Raka Rizki Aulianita, Rizki Robbie Christhover Robertus Suraji Rosliana, Siti Rusdiansyah Rusdiansyah Salwa Nabiila Pramuhesti Samsiana , Seta Sandy Satyo Prihatin Santoso , Muhammad Reinaldy Sanusi, Beno Aditya Saputra , Faisal Adi Saputra, Fachrullyanta Adi Sari , Rafika SATRIYAS ILYAS Septi Eka Hardyana Septia, Dwi Yoga Seta Samsiana Seta Samsiana Seta Samsiana Seta Samsiana Seta Samsiana Setiawan, Andy Achmad Hendhar Setyowati Srie Gunarti, Anita Shadriyah , Shadriyah Silaen, Oriza Sativa Dinauni Siti Masripah, Siti Siti Rosliana SITI SETIAWATI Sohee Minsun Kim Solikin Solikin Solikin Solikin Sri Rejeki Sri Sureni Sugeng Murdowo Sugiyatno , Sugiyatno Sugiyatno Sugiyatno Sugiyatno Sugiyatno Sunandar Sunandar Syadhaffa Gedriyansah Syafina, Prilia Hashifah Syahbaniar Rofiah Syahfitri, Intan Cahya Tambun, Jerisman Jhon Wesli Tia Monisya Afriyanti Trisumeikra, I Komang Arya Tumbur Togu Tyastuti Sri Lestari Tyastuti Sri Lestari Umi Salamah Wicaksono, Naufal Eka Wida Prima Mustika Wiguna, Bhagaskara Farhan Wijaya, Indra Yana, Anisa Feby Yessi Rahmawati Yugo Bhekti Utomo Yusuf, Ajif Yunizar Pratama