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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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Prediksi Perubahan Penggunaan Lahan dan Pola Berdasarkan Citra Landsat Multi Waktu dengan Land Change Modeler (LCM) Herlawati, Herlawati; Nidaul Khasanah, Fata; Dina Atika, Prima; Sari, Rafika; Handayanto, Rahmadya Trias
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5139

Abstract

Land use/cover greatly affect the quality of an area. Therefore, many regional planners need assistance byother fields, such as geoinformatics, computer science, environment, and others. Although prediction and forecasting have been widely studied, in regardto real conditions (geospatial)itstill needmoredevelopment, especially thoseinvolving a combination of regional types, such as urban and suburban areas. This study uses a remote sensing base and geographic information system in predicting land in the city and district of Bekasi, West Java, Indonesia. With two scenarios compared (business as usual and vegetation conservation), the model that has been created and validated (with an AUC accuracy result of 0.828) is used to predict land use change until 2030. Scenarios with vegetation conservation are able to keep green areas to switch to land types others, such as buildings and industry
Analisis Sentimen Pada Situs Google Review dengan Naïve Bayes dan Support Vector Machine Handayanto, Rahmadya Trias; Herlawati, Herlawati; Atika, Prima Dina; Khasanah, Fata Nidaul; Yusuf, Ajif Yunizar Pratama; Septia, Dwi Yoga
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 2 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i2.6280

Abstract

Tourism is the sources of income which is influenced by customer satisfaction. One way to know customer satisfaction is feedback, one of which is a review using an application. One of the feedback applications is Google Review. Such applications are have been widely used, for example in this study in this case study, Summarecon Mal Bekasi, can reach 60,000 comments. To find out the sentiment of the large number of comments, it is necessary to use computational tools. The current research applies sentiment analysis using the Naïve Bayes method and the Support Vector Machine. Data retrieval is done by web scrapping technique. Furthermore, the comment data is processed by pre-processing and labelling using the Lexicon dictionary. The process of applying sentiment analysis is carried out to determine whether the comments are positive or negative. In this study, the accuracy of the Naïve Bayes and Support Vector Machine methods in conducting sentiment analysis on the Summarecon Mal Bekasi review with a data of 2,143 comments with an accuracy for Naïve Bayes and Support Vector Machine 80.95% and 100% respectively. A Jason-style application is built to show the implementation in Flask framework. Keywords:
Analisis Sentimen Mengenai Gangguan Bipolar Pada Twitter Menggunakan Algoritma Naïve Bayes Silaen, Oriza Sativa Dinauni; Herlawati, Herlawati; Rasim, Rasim
Jurnal Komtika (Komputasi dan Informatika) Vol 6 No 2 (2022)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v6i2.8198

Abstract

Bipolar disorder is one of the world's most common mental health disorders. To find out public sentiment regarding bipolar disorder, sentiment analysis is carried out through social media to analyze positive or negative sentiments with the aim of maintaining positive sentiment towards the problem of bipolar disorder. Twitter is a social media that is often used to exchange information, discuss, and even express emotions. The emotions of Twitter users can be called sentiment. Sentiment analysis is also carried out to see opinions or tendencies towards an opinion. Opinion tendencies can be in the form of positive or negative sentiments. The data used in this study uses the bipolar keyword. There are 2177 tweets data that were successfully obtained in the crawling process using API key access from Twitter developers, after which the data will be processed using preprocessing. The comparison of the presentations obtained is 70.92% expressing a negative opinion and 29.08% expressing a favorable opinion. The analysis results in this study using the nave Bayes algorithm is with an accuracy value of 92.110092%.
Prediksi Curah Hujan Di Kabupaten Bogor Menggunakan Long Short-Term Memory Dan Gemma 2 Wijaya, Indra; Herlawati, Herlawati; Sari, Rafika
Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 1 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v9i1.13639

Abstract

Bogor Regency is an area that often experiences prolonged rainfall, especially during the rainy season. High rainfall causes problems such as floods and landslides. Therefore, accurate rainfall prediction is important for various needs, especially in disaster mitigation. This study aims to implement the Long Short-Term Memory (LSTM) algorithm as a model for prediction of historical rainfall data and use the Large Language Model (LLM) GEMMA 2 to provide interpretation of prediction results and recommendations based on the prediction results. The methods used include data collection from the BMKG online data website totaling 1804 data, data pre-processing, model building, model performance evaluation, and interpretation of results using LLM. The results of this study show that LSTM is able to produce the best performance by showing MSE 201.92 mm², Root Mean Square Error (RMSE) of 14.21 mm. the RMSE value shows an average error of 14.21 mm. In addition, the interpretation provided by LLM GEEMA 2 to help understand the prediction and provide practical recommendations for disaster mitigation due to rainfall.
Klasifikasi Sentimen iPhone Bekas di Tokopedia menggunakan Naïve Bayes dan Support Vector Machine Novianto, Krisna; Herlawati, Herlawati; Hidayat, Agus
Jurnal Ilmiah FIFO Vol 17, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2025.v17i2.010

Abstract

Kenaikan harga iPhone baru mendorong meningkatnya pembelian iPhone second di platform e-commerce seperti Tokopedia. Namun, konsumen masih menghadapi berbagai risiko terkait kondisi perangkat, performa komponen, dan keaslian yang umumnya teridentifikasi melalui ulasan pengguna. Penelitian ini bertujuan menganalisis sentimen dari 1.863 ulasan iPhone second untuk memperoleh gambaran objektif mengenai pengalaman konsumen. Teks ulasan diproses menggunakan TF-IDF sebagai representasi fitur dan SMOTE untuk mengatasi ketidakseimbangan kelas. Dua algoritma Naive Bayes dan Support Vector Machine (SVM) dibandingkan untuk menilai efektivitas klasifikasi. Hasil pengujian menunjukkan bahwa SVM memberikan performa terbaik dengan akurasi 96%, melampaui Naive Bayes yang mencapai 93%. Analisis lebih lanjut menemukan bahwa ulasan positif umumnya berkaitan dengan kualitas fisik dan kecepatan pengiriman, sedangkan ulasan negatif banyak menyoroti isu teknis serta keaslian perangkat. Penelitian ini berkontribusi pada penguatan literatur analisis sentimen e-commerce melalui evaluasi komprehensif terhadap kombinasi TF-IDF + SMOTE serta perbandingan performa Naive Bayes dan SVM dalam klasifikasi opini konsumen. Temuan ini menyediakan dasar empiris untuk penelitian lanjutan mengenai penilaian kualitas produk bekas berbasis ulasan daring.
Analisis Sentimen Ulasan Produk Sneakers Lokal Pada Tokopedia Menggunakan Algoritma Naïve Bayes dan Support Vector Machine Trisumeikra, I Komang Arya; Herlawati, Herlawati; Hidayat, Agus
Journal of Students‘ Research in Computer Science Vol. 6 No. 2 (2025): November 2025
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/5wew3w31

Abstract

transformation in various sectors, including the fashion industry, especially sneakers. Sneakers are now a symbol of modern lifestyle and global trends, with brands such as Nike and Adidas dominating the market. However, high prices are an obstacle for many Indonesian consumers. This opens up opportunities for local brands to offer quality products at affordable prices through e-commerce such as Tokopedia, the second highest traffic platform in Indonesia. The research analyzed sentiment from 1,032 consumer reviews of local sneakers from five stores: NAH Project, Aerostreet, Geoff Max, Ventela, and Brodo. The analysis was conducted using Naïve Bayes and Support Vector Machine (SVM) algorithms. The SVM evaluation results produced the highest accuracy of 98%, compared to Naïve Bayes which reached 96%. This best model is implemented in a web-based application to analyze the sentiment of new reviews, to assess the perceived quality and consumer satisfaction of local sneakers products on Tokopedia.
Model Prediksi Kondisi Kesehatan dari Data Medical Check-Up Menggunakan K-Nearest Neighbors dan Decision Tree Cahyaaty, Tata Arya; Herlawati, Herlawati; Setiawan, Andy Achmad Hendhar
Journal of Students‘ Research in Computer Science Vol. 6 No. 2 (2025): November 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/tvt7s936

Abstract

Medical Check-Up (MCU) is an essential procedure for the early detection of health disorders. However, manual analysis of MCU results requires time and may be subject to the interpretation of medical personnel. This study aims to develop an automatic classification system to predict health conditions based on MCU results using the K-Nearest Neighbors (KNN) and Decision Tree algorithms. The MCU data used includes blood pressure, body temperature, heart rate, as well as heart and blood pressure assessments. The models were trained and evaluated using the CRISP-DM methodology. The results show that the Decision Tree achieved an accuracy of 91.31%, while KNN achieved an accuracy of 89.75%. This system is implemented as a web-based application with a simple user interface to support the early diagnosis process at RS EMC Cibitung.
Segmentasi Berbasis Data Time Series Penjualan Produk Kopi Menggunakan Algoritma K-Means Anggaini, Meri; Herlawati, Herlawati; Purnomo, Rakhmat
Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v9i2.15336

Abstract

Coffee shops are businesses in the Food and Beverage (F&B) sector that contribute 7.15% to Indonesia's economy. The high demand for coffee has led to increasingly fierce competition. Kanae Coffee & Space in Bekasi faces challenges in maintaining customer loyalty and managing unpredictable demand. This study aims to apply the K-Means algorithm to cluster coffee products based on time series sales data, using the 6-step CRISP-DM methodology. The number of clusters was determined using the elbow method and confirmed with a silhouette coefficient of 0.5916 (good structure). The analysis resulted in five clusters with distinct characteristics: Cluster 0 (very low demand, stable trend, very high price), Cluster 1 (very high demand but sharply declining trend, very low price), Cluster 2 (moderately high demand, moderately stable trend, moderate price), Cluster 3 (moderate demand, slowly declining trend, moderately high price), and Cluster 4 (low demand, stable trend, moderately low price). These segmentation results are expected to serve as the basis for more effective marketing strategies and product management.
Penyuluhan Dan Pelatihan Keamanan Data Digital Bagi Masyarakat Dari Ancaman Kejahatan Cyber Herlawati, Herlawati; Fandiansyah, Rafly; Ningrum, Mirza Cahya; Magdalena, Caroline Julyana; Gymnastiar, Muhammad; Wicaksono, Naufal Eka; Fadilah, Naufal Arif; Rismayana, Raka; Santoso , Muhammad Reinaldy; Purnama, Putra Aldi; Pahrizal, Pahrizal; Nurcholis, Nurcholis
Journal Of Computer Science Contributions (JUCOSCO) Vol. 6 No. 1 (2025): Januari 2026
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/va67rw83

Abstract

The rapid development of information technology has led to increased use of digital media by the community, which has not been accompanied by adequate understanding of digital data security. This condition makes the community vulnerable to various cybercrime threats such as online fraud, personal data theft, phishing, and the spread of hoaxes. This community service activity aims to improve the digital literacy and awareness of residents of Taman Kebayoran Housing Area RW 013, Setiamekar Village, Bekasi Regency, regarding the importance of digital data security through counseling and training programs. The implementation methods include initial socialization, face to face counseling, digital data security training, and the development of a Housing Profile Website as an official and verified information medium. The results show an increase in community understanding of types of cyber threats, methods to identify false information, and preventive measures against digital fraud. In addition, the existence of the housing profile website is considered effective in supporting information transparency and strengthening communication between community administrators and residents. The originality of this activity lies in the integration of community based cybersecurity education with the development of a local digital platform that functions as a literacy and official information medium. This program is expected to serve as a sustainable community service model in building a safe and responsible digital ecosystem at the neighborhood level.
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