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All Journal TEKNIK INFORMATIKA JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Mantik Penusa MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Varian Jurnal SASAK : Desain Visual dan Komunikasi Jurnal Bumigora Information Technology (BITe) Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Abdi Masyarakat Indonesia Indo-MathEdu Intellectuals Journal Abdi Widya: Jurnal Pengabdian Masyarakat Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Jurnal Ilmiah Pengabdian dan Inovasi INTAN CENDEKIA: JURNAL PENGABDIAN MASYARAKAT EMPOWERMENT: Journal of Community Practice
Anthony Anggrawan
Ilmu Komputer, Fakultas Teknik, Universitas Bumigora
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Peningkatan Pengetahuan Bidang Komputer, Desain, dan Hukun Pada Komunitas Ajar Wali Kasim, Sunardy; Anggrawan, Anthony; Satria, Christofer; Rosikhu, Muhammad; Sakti, Lanang; Mardedi, Lalu Zazuli Azhar; Haryono, Haryono; Cahyadi, Irwan
INTAN CENDEKIA (Jurnal Pengabdian Masyarakat) Vol 3, No 1 Juni (2022): INTAN CENDEKIA: JURNAL PENGABDIAN MASYARAKAT
Publisher : Yayasan Pendidikan Intan Cendekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47165/intancendekia.v3i1.101

Abstract

Tujuan pengabdian ini untuk memberikan pengetahuan umum kepada masyarakat tentang komputer, desain dan hukum. Pengabdian ini bekerjasama dengan komunitas budaya ajar wali dan komunitas budaya Darmayasa sebagai mitra dalam memberikan sosialisasi pentingnnya pengetahuan di bidang teknologi khususnya komputer dan memahami hukum yang berlaku baik dalam bentuk hukum adat dan negara. Metode yang digunakan yaitu sosialisasi dengan memaparkan materi dan diskusi terbuka pada masyarakat dan anggota komunitas budaya ajar wali dan komunitas budaya Darmayasa. Hasil kegiatan ini untuk meningkatkan pengetahuan masyarakat tentang pentingnya teknologi komputer di era digital yang dalam hal ini untuk mendukung administrasi, pembuatan desain untuk promosi baik dimedia cetak, media sosial dan media promosi/publikasi lainnya. Sedangkan dibidang hukum sendiri hasil yang di peroleh dari pelaksanaan pengabdian masyarakat ini adalah semakin paham dan sadarnya masyarakat terutama komunitas budaya akan peraturan dan ketentuah hukum, baik hukum adat maupun hukum negara.
Pelatihan Media Pembelajaran Berbasis Animasi Interaktif Pada Sekolah Menengah Kejuruan Christofer Satria; Hasbullah Hasbullah; Anthony Anggrawan; I Nyoman Yoga Sumadewa; I Nyoman Subudiartha
Jurnal Ilmiah Pengabdian dan Inovasi Vol. 3 No. 2 (2024): Jurnal Ilmiah Pengabdian dan Inovasi (Desember)
Publisher : Insan Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57248/jilpi.v3i2.471

Abstract

Perkembangan teknologi AI yang pesat mempermudah pengembangan media, namun tidak semua guru SMK mampu mengikutinya. Tulisan ini bertujuan mendampingi pembuatan media pembelajaran animasi interaktif untuk meningkatkan kualitas pembelajaran di SMK Kota Mataram. Media ini diharapkan meningkatkan motivasi, partisipasi, dan pemahaman siswa terhadap materi teknis yang kompleks. Metode pengembangan yang digunakan dalam pengabdian ini adalah model Klasikal dan simulasi yang meliputi tahapan analisis kebutuhan, perancangan media, pengembangan produk, uji coba lapangan, serta evaluasi dan revisi. Hasilnya, media animasi interaktif dinilai efektif meningkatkan pemahaman konsep, menarik minat siswa, dan membuat mereka lebih aktif dalam belajar. Uji coba menunjukkan peningkatan signifikan pada hasil belajar dibanding metode konvensional. Kesimpulannya, media animasi interaktif memberikan dampak positif pada kualitas pembelajaran di SMK dan diharapkan dapat diadopsi lebih luas untuk mendukung pembelajaran inovatif dan efektif.
Multi-Algorithm Approach to Enhancing Social Assistance Efficiency Through Accurate Poverty Classification Christofer Satria; Peter Wijaya Sugijanto; Anthony Anggrawan; I Nyoman Yoga Sumadewa; Aprilia Dwi Dayani; Rini Anggriani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4275

Abstract

The determination of poverty status in Lombok Utara district depends on criteria such as income, access to health and education services, and housing conditions. These factors are crucial for assessing the level of community welfare and guiding the allocation of social assistance by the district government. The purpose of this study is to address the gap by utilizing advanced data mining techniques to improve the accuracy of poverty status classification in North Lombok, thereby informing more effective social assistance policies. The method used in this research is the Random Forest (RF), K-Nearest Neighbor (KNN) and Naïve Bayes with split data 80% data training and 20% data testing. The finding indicated that the machine learning model the RF algorithm, which achieved an accuracy rate of 82.56%, proved to play an important role in this process by effectively distinguishing between different categories of poverty based on these criteria. In comparison, the KNN algorithm achieved an accuracy of 70.94% and the Naïve Bayes model achieved an accuracy of 53.47%. It means that the machine learning model using the RF algorithm has more accurate accuracy than the KNN and Naïve Bayes algorithm in predicting or recommending Recipients of Social Assistance from the District Government. The implication is that RF machine learning can help the role of social service officers in predicting the economic status of the community. The high accuracy of the RF algorithm enhances its role in informing targeted policy decisions and optimizing the effectiveness of social assistance programs. Nonetheless, continuous improvement is essential to refine the model's predictive capabilities and ensure the accuracy and reliability of poverty assessments. These continuous improvements are essential to effectively alleviate poverty and break the cycle of socio-economic disparities in the region.
Prediction of Electricity Usage with Back-propagation Neural Network Anthony Anggrawan; Hairani Hairani; M. Ade Candra
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 1 No. 1 (2022): March 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v1i1.1722

Abstract

The use of electricity has become a need that is increasing day by day. So it is not surprising that the problem of using electricity has attracted the attention of many researchers to research it. Electricity users make various efforts and ways to save on the use of electrical energy. One of them is saving electricity usage by electricity users using electrical energy-efficient equipment. That is why the previous research confirms the need for interventions to reduce the use of electrical energy. Therefore, this study aims to predict electricity use and measure the performance of the anticipated results of electricity use. This study uses the back-propagation method in predicting the use of electricity. This study concluded that the backpropagation architectural model with better performance is the six hidden layer architecture, 0.4 learning rate, and the Root Means Square Error (RMSE) value of 0.203424. Meanwhile, the training data test results get the best architectural model on hidden layer 8 with a learning rate of 0.3 with an RMSE performance value of 0.035811. The prediction results show that the prediction of electricity consumption is close to the actual data of actual electricity consumption.
Enhancing Mental Illness Predictions: Analyzing Trends Using Multiple Linear Regression and Neural Network Backpropagation Riosatria Riosatria; Hairani Hairani; Anthony Anggrawan; Moch. Syahrir
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 2 (2024): September 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i2.4391

Abstract

The increasing number of mental health cases caused by various factors such as social changes, economic pressures, and technological advancements has made it difficult to accurately predict the number of cases, hindering prevention and early intervention efforts. Therefore, developing more accurate, data-driven predictive models is necessary to improve the effectiveness of prevention and intervention. This study aims to develop a predictive model for the number of mental health cases using Multiple Linear Regression and Neural Network Backpropagation methods. The study employs two predictive methods, Multiple Linear Regression and Neural Network Backpropagation to forecast future trends in the number of mental health cases. The findings reveal that the Neural Network Backpropagation method provides more accurate predictions than Multiple Linear Regression in forecasting mental health case trends. Specifically, the Neural Network Backpropagation method resulted in an MAE of 111.39 and a MAPE of 1.77%, while the Multiple Linear Regression method produced an MAE of 115.24 and a MAPE of 1.83%. Thus, the implication of this study is that the Neural Network Backpropagation method can be utilized to predict trends in the number of mental health cases due to its ability to provide highly accurate predictions.
Clustering Analysis of Umrah Pilgrim Data Based on the K-Medoid Method Dias Nabila Huda; Anthony Anggrawan; Hairani Hairani
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 2 (2024): September 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i2.4601

Abstract

The Umrah pilgrimage is becoming increasingly popular among Indonesians, with millions of participants yearly. This trend creates a need for service providers to understand the characteristics of pilgrims to improve service quality, marketing strategies, and competitiveness. Analyzing data on pilgrims helps service providers develop more effective strategies and tailor packages to match their needs, ensuring competitiveness in a growing market. This study aims to clusters Umrah pilgrims based on age, gender, district, and chosen package using the K-Medoid clustering method. This research uses the K-Medoid method for the reason that it is more resistant to noise and outliers compared to other clustering methods. The most centrally located point in the data set is called a ”medoid,” which is an object in a cluster that has the lowest difference to all other objects in the cluster. The results of this study are that the K-Medoid method successfully grouped pilgrims into three clusters: Cluster 1 with 63 members, Cluster 2 with 25 members, and Cluster 3 with 25 members. The findings indicate that the Milad Mastour package is preferred by older pilgrims, primarily from Mataram and West Lombok. The Arbain package is favored by younger pilgrims from the same regions, while adult pilgrims mostly choose the Regular package. The implication of this research is that it can provide insights for service providers to design more specific programs that align with the profiles of pilgrims based on age and district.
Optimizing Sentiment Analysis for Lombok Tourism Using SMOTE and Chi-Square with Machine Learning Hairani; Anggrawan, Anthony; Muhammad Ridho Akbar; Khasnur Hidjah; Muhammad Innuddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6623

Abstract

Tourism is a vital economic sector for Lombok Island, which is renowned for its natural beauty and cultural richness as a top destination. The rapid growth of tourism in Lombok requires a deep understanding of tourists' perceptions and sentiments to ensure an optimal service quality. The sentiment analysis of online reviews is valuable for identifying service strengths and weaknesses and addressing tourists' needs more effectively. This not only enhances tourist satisfaction, but also aids in the design of more effective marketing strategies. However, text data analysis from online reviews presents unique challenges such as noise, class imbalance, and numerous features that may affect classification results. Therefore, this study aims to classify tourist sentiment toward Lombok tourism using machine learning methods combined with feature selection and oversampling techniques. This study focuses on optimizing sentiment analysis of tourism-related tweets using a combination of SMOTE oversampling and Chi-Square feature selection on improving classification performance without hyperparameter tuning. The study applies machine learning methods, such as SVM and Naïve Bayes, with feature selection and oversampling using Chi-Square and SMOTE. The dataset used was sentiment data regarding Lombok tourism obtained from Twitter in 2023, consisting of 940 instances divided into three classes: Negative, Neutral, and Positive. The research findings show that the use of SMOTE and Chi-Square can improve the accuracy of the SVM and Naive Bayes methods. Without optimization, the SVM method achieved an accuracy of 73.93% and a Naive Bayes of 67.02%. After optimization with SMOTE and Chi-Square, the accuracy increased for SVM by 90% and Naive Bayes by 84% to classify tourist sentiment towards Lombok tourism. The implications indicate that combining data balancing using SMOTE with feature selection via Chi-Square effectively improves the performance of sentiment classification models for tourist opinions on Lombok's tourism.
Penggunaan Aplikasi Duolingo dalam Meningkatkan Kosakata Bahasa Inggris Pada Siswa Di Sekolah Rahmawati, Lela; Anggrawan, Anthony; Hastuti, Hilda; Aprianto, Dedi; Alfilail, Nur
ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 5 No. 2 (2025): ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/adma.v5i2.4623

Abstract

The aim of community service activities is to provide English language training to students at Madrasah Aliyah Negeri Program Keagamaan (MAN-PK) MAN 2 Mataram. This learning training uses the Duolinglo Application. This training or course involves 2 classes of class X students totaling 50 participants in September and November 2024 at MAN PK MAN 2 Mataram. Each group was asked to use the Duolingo application to integrate English learning. This application is a gamification application, meaning users can learn English like playing games because this application uses audio-visual methods in learning. The methods used in this training are lecture, discussion and direct practice. The result of this service activity is that there is an increase in participants' skills and knowledge in speaking and writing using the Duolingo application. The results of evaluation and monitoring by applying the average post-test score show that the average post-test score (72.18) is greater than the average pretest score (35.78). The implications of community service activities can increase students' knowledge of English at school by using the Duolingo application.
Optimalisasi Kemampuan Analisis Data Kuantitatif Mahasiswa Melalui Pelatihan SPSS di Universitas Bumigora Sri Astuti Iriyani; Anthony Anggrawan; Elyakim Nova Supriyedi Patty; Sutarman; Lalu Busyairi Muhsin
ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 6 No. 1 (2025): ADMA: Jurnal Pengabdian dan Pemberdayaan Mayarakat
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/adma.v6i1.5086

Abstract

PKM ini bertujuan untuk meningkatkan kemampuan akademik dan kompetensi teknis mahasiswa dalam analisis data kuantitatif melalui penggunaan perangkat lunak Statistical Package for the Social Sciences (SPSS). Metode yang diterapkan adalah pendekatan partisipatif dan edukatif, yang melibatkan 25 mahasiswa dari Fakultas Pendidikan Universitas Bumigora. Pendekatan partisipatif memberikan kesempatan kepada mahasiswa untuk terlibat secara langsung dalam setiap tahapan pelatihan, sementara pendekatan edukatif menekankan pembelajaran berbasis praktik yang bertujuan untuk memperkuat keterampilan teknis mahasiswa dalam penggunaan perangkat lunak SPSS. Evaluasi efektivitas pelatihan dilakukan dengan pre-test dan post-test yang diimplementasikan sebelum dan setelah pelatihan berlangsung. Hasil analisis data menunjukkan adanya peningkatan yang signifikan pada skor posttest dibandingkan dengan pre-test, yang mengindikasikan bahwa pelatihan ini berhasil memperkuat pemahaman mahasiswa terhadap konsep dasar statistik dan penerapannya. Peningkatan tersebut juga mencerminkan kemajuan dalam keterampilan teknis mahasiswa dalam mengolah dan menganalisis data kuantitatif. Diharapkan kegiatan ini dapat meningkatkan kompetensi mahasiswa dalam analisis data kuantitatif dan memberikan dasar yang kuat untuk penerapan pengetahuan tersebut dalam konteks akademik dan profesional.
Pengembangan LMS Interaktif untuk Praktikum Komunikasi Jaringan Anggrawan, Anthony; Zulkipli, Zulkipli; Supriantono, Herman; v, Sovian; Ariq, Tomy; Lutfie, Muhammad Hilal Mumtaz
Indo-MathEdu Intellectuals Journal Vol. 6 No. 5 (2025): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v6i5.3872

Abstract

The development of information technology brings significant transformation in the field of education, especially through the implementation of e-learning. One concrete manifestation of the digitization of learning is the use of Learning Management System (LMS), which serves as an interactive platform for managing, delivering, and evaluating the teaching and learning process. This article aims to examine the development and implementation of LMS in the context of learning, particularly in network communication practices, through a literature study approach. Data is collected from various national and international literature discussing the effectiveness, challenges, and innovations in the use of LMS. The main data source for this research is scientific articles sourced from databases in Google Scholar. The analysis technique used is qualitative content analysis with a descriptive approach. The results of the analysis show that LMS can improve the quality of learning, encourage active participation of students, and provide flexibility in delivering material. The analyzed studies also emphasize the importance of the integration between technical aspects, pedagogical aspects, and user characteristics in designing an effective and adaptive LMS. Thus, the development of interactive project-based and multimedia LMS is highly relevant to support practical learning in network communication in the digital era.
Co-Authors Abdul Rahim Ahmat Adil Alfilail, Nur Anggriani, Rini Aprilia Dwi Dayani Ariq, Tomy Ayu Dasriani, Ni Gusti Azhar, Raisul Azhari Azhari Bidari Andaru Widhi Cahyadi, Irwan Canggih Wahyu Rinaldi Cecep Kusmana christofer satria Christofer Satria Dadang Priyanto Dadang Pyanto Dafa Awanta Dayani, Aprilia Dwi Dedi Aprianto Dewa Ayu Oki Astarini Diah Supatmiwati Dian Syafitri Chani Saputri Dias Nabila Huda Didiharyono, D. Donny Kurniawan Dwi Kurnianingsih Dyah Susilowati Dyah Susilowati Efrizoni, Lusiana Elyakim Nova Supriyedi Patty, Elyakim Nova Supriyedi Erwin Suhendra Fadiel Rahmad Hidayat Hairani Hairani Haryono Haryono Hasbullah Hasbullah Hasbullah Helna Wardhana Hengki Tamando Sihotang Herawati, Baiq Candra Hilda Hastuti Huda, Dias Nabila Husain Husain I Nyoman Subudiartha I Nyoman Yoga Sumadewa I Nyoman Yoga Sumadewa Ikang Murapi Irwan Cahyadi Jean Suciasti Gunawan Junendri Ardian Kamil, Wahyu Katarina Katarina Khairan marzuki Khasnur Hidjah Kurniadin Abd Latif Lalau Ganda Rady Putra Lalu Ganda Rady Putra Lanang Sakti Lutfie, Muhammad Hilal Mumtaz M Najmul Fadli M. Ade Candra M. Thontowi Jauhari Mardedi, Lalu Zazuli Azhar Mayadi Mayadi Mayadi Mayadi Miswaty, Titik Ceriyani Mokhammad Nurkholis Abdillah Muhammad Innuddin Muhammad Ridho Akbar Muhammad Rosikhu MUHAMMAD TAJUDDIN Muhammad Zaki Pahrul Hadi Muhammad Zulfikri Muhsin, Lalu Busyairi Nurhidayati, Maulida Nurul Azmi Nurul Hidayah Peter Wijaya Sugijanto Primajati, Gilang Purnama, Baiq Kartika Putu Tisna Putra R. Ayu Ida Aryani Raden Bagus Faizal Irani Sidharta Rahmat Maulana Rahmawati, Lela Rahmiati, Baiq Fitria Rini Anggriani Rini Anggriani Riosatria Riosatria Riosatria, Riosatria Santoso, Heroe Sarjon Defit Satuang Satuang Sirojul Hadi Siti Soraya Sri Astuti Iriyani Sugijanto, Peter Wijaya Sunardy Kasim Supriantono, Herman Sutarman Syahrir, Moch. Syamsurrijal Syamsurrijal Tomi Tri Sujaka Triwijoyo, Bambang Krismono v, Sovian Veithzal Rivai Zainal Wayan Canny Naktiany Wenny Wijaya Wiya Suktiningsih Zulkipli Zulkipli