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All Journal Jurnal Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI EXPLORE CCIT (Creative Communication and Innovative Technology) Journal International Journal of Informatics and Computation EXPLORE Jurnal Teknik Informatika dan Teknologi Informasi Safari : Jurnal Pengabdian Masyarakat Indonesia Journal Of Computer Science And Technology Jurnal Rekayasa Sistem Informasi dan Teknologi Journal of Computer Science and Information Technology Jurnal Kecerdasan Buatan dan Teknologi Informasi Journal of Data Analytics, Information, and Computer Science (JDAICS) Jurnal Computer and Technology Explore
Suryadi, Emi
Universitas Teknologi Mataram
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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PERANCANGAN SMART FARMER PADA TANAMAN PADI MENGGUNAKAN ESP8266 BERBASIS INTERNET OF THINGS (IOT) Juniyardi, Lalu; Suryadi, Emi; Akbar, Ardiyallah; Samsumar, Lalu Delsi
Journal of Data Analytics, Information, and Computer Science Vol. 1 No. 4 (2024): Oktober
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jdaics.v1i4.1328

Abstract

Masalah hama burung pada tanaman padi merupakan masalah serius bagi petani, karena dapat mengakibatkan penurunan hasil panen yang signifikan. Penelitian ini bertujuan untuk sistem pengusir hama burung berbasis Internet of Things (IoT) dalam judul Smart Farmer Pada Tanaman Padi Menggunakan ESP8266 Berbasis IoT yang efektif dan efisien untuk melindungi tanaman padi. Sistem ini dirancang untuk mendeteksi kehadiran burung secara real-time dan mengusirnya menggunakan kombinasi suara dan gerakan. Metodologi penelitian meliputi tahap perancangan perangkat keras dan perangkat lunak, pengujian di lapangan, dan analisis data. Sistem ini menggunakan sensor PIR (Passive Infrared) untuk deteksi gerakan dari burung, NodeMCU ESP8266 sebagai pengendali utama, dan DFPlayer Mini yang terhubung dengan speaker untuk memutar suara pengusir burung. Sistem juga dilengkapi dengan dua motor servo yang menggerakkan objek yang menakuti burung. Semua komponen ini diintegrasikan melalui jaringan IoT untuk memungkinkan pemantauan dan pengendalian jarak jauh. Hasil pengujian di lapangan menunjukkan bahwa sistem pengusir hama burung berbasis IoT ini mampu mendeteksi kehadiran burung dengan akurasi yang tinggi dan mengusirnya secara efektif. Penggunaan kombinasi suara dan gerakan terbukti lebih efektif dibandingkan dengan metode konvensional. Sistem ini juga dapat dipantau dan dikendalikan secara real-time melalui aplikasi berbasis web, memberikan kemudahan bagi petani dalam mengelola dan mengawasi perangkat pengusir hama.
IMPLEMENTASI VULNERABILITY ASSESSMENT OWASP (OPEN WEB APPLICATION SECURITY PROJECT) PADA WEBSITE UNIVERSITAS TEKNOLOGI MATARAM Supriadi, Dedi; Suryadi, Emi; Muslim, Rudi; Samsumar, Lalu Delsi
Journal of Data Analytics, Information, and Computer Science Vol. 1 No. 4 (2024): Oktober
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jdaics.v1i4.1368

Abstract

Teknologi informasi dan komunikasi mengalami peningkatan signifikan selama beberapa dekade terakhir. Website sering kali menjadi target serangan siber karena terdapat kerentanan yang bisa dimanfaatkan oleh penyerang. Penelitian ini bertujuan untuk melakukan Vulnerability Assessment (VA) pada website Universitas Teknologi Mataram dengan mengikuti panduan dari Open Web Application Security Project (OWASP). OWASP menyediakan daftar sepuluh besar kerentanan yang sering ditemukan didalam aplikasi web, seperti SQL Injection, XSS (Cross-Site Scripting), dan CSRF (Cross-Site Request Forgery). Penelitian ini menggunakan metode Vulnerability Assessment dan Penetration Testing (VAPT), yang terdiri dari beberapa tahap: pengumpulan informasi, pemindaian untuk mengidentifikasi celah keamanan, eksploitasi celah, serta pembuatan laporan. Tools yang digunakan antara lain OWASPZAP dan Burp Suite untuk mendeteksi dan pengujian kerentanan. Penelitian ini menghasilkan laporan yang mengidentifikasi tiga kerentanan dengan level sedang, empat kerentanan dengan level rendah, serta tidak ada kerentanan dengan level tinggi. Setelah pemindaian, hasil pengujian menunjukkan bahwa serangan Clickjacking berhasil dieksploitasi, sementara serangan XSS tidak berhasil dilakukan, menunjukkan adanya mekanisme pertahanan yang baik terhadap XSS. Selain itu, ditemukan beberapa kelemahan dalam konfigurasi aplikasi. Solusi perbaikan yang direkomendasikan disesuaikan dengan standar keamanan OWASP. Dengan dilakukannya VAPT ini, diharapkan pengelola website Universitas Teknologi Mataram dapat meningkatkan keamanan dan mengurangi risiko serangan siber yang berpotensi merugikan. Implementasi OWASP sebagai panduan pengujian keamanan terbukti efisien dalam mendeteksi serta mengatasi celah keamanan dalam aplikasi web.
Sentiment Analysis of a 271 Trillion Rupiahs Corruption Case Using LSTM Selamet Riadi; Rudi Muslim; Emi Suryadi; Karina Nurwijayanti; M. Zulpahmi; Muhamad Masjun Efendi; Bahtiar Imran
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.104

Abstract

Corruption is one of the most pressing issues in Indonesia, significantly affecting public trust in governance and the nation’s development. Among the many corruption cases that have surfaced, the recent 271 trillion rupiah corruption case has drawn widespread attention and public discourse. Understanding the public's perception and sentiment regarding such cases can provide valuable insights into how these issues impact society. Researchers identified an opportunity to leverage sentiment analysis as a method to capture and analyze public sentiment in this context. The dataset for this study was collected from the social media platform Twitter (X) using a data crawling technique. Prior to analysis, preprocessing was performed to clean and prepare the data. After preprocessing, the data was categorized into three sentiment labels: negative, positive, and neutral. To perform sentiment classification, this study utilized the LSTM (Long Short-Term Memory) algorithm, a deep learning method particularly suited for sequential data analysis. The model was trained over a total of 10 epochs. The classification results demonstrated that the LSTM algorithm achieved an accuracy of 0.9365 at the 10th epoch, showcasing its effectiveness in analyzing public sentiment regarding 271 trillion rupiah corruption issues.
Rancang Bangun Sistem Pemantauan Kualitas Air Bor Berbasis Internet Of Things Nur Fauziah; Emi Suryadi; Ardiyallah Akbar
Journal of Computer Science and Technology (JOCSTEC) Vol 3 No 2 (2025): JOCSTEC - Mei
Publisher : PT. Padang Tekno Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59435/jocstec.v3i2.430

Abstract

Air adalah senyawa kimia yang sangat vital bagi kehidupan, dan salah satu metode untuk mendapatkan air bersih adalah melalui sumur bor. Meskipun air dari sumur bor sering digunakan untuk keperluan sehari-hari seperti mandi, mencuci, dan memasak, kualitasnya sering kali kurang diperhatikan. Dengan adanya teknologi Internet of Things (IoT), pemantauan kualitas air dapat dilakukan dengan lebih mudah dan efisien. Penelitian ini menggunakan metode prototipe, dengan pengumpulan data yang dilakukan melalui studi pustaka dan observasi. Hasil analisis menunjukkan bahwa sistem pemantauan kualitas air bor berfungsi dengan baik, menampilkan data suhu berkisar antara 10,51-23,99°C (standar suhu normal air bersih idealnya 10-25°C), kekeruhan sebesar 26,52-29,50 NTU (standar maksimum kekeruhan air adalah 25 NTU), dan pH antara 5,17-9,19 (standar pH air adalah 6,5-8,5), yang dapat diakses melalui aplikasi Blynk. Kesimpulannya, air dari sumur bor di beberapa lokasi sampel memenuhi standar kualitas yang ditetapkan oleh Dinas Kesehatan, dengan Arduino dan NodeMCU berfungsi secara optimal dalam memproses serta mengirim data secara real-time.
Penguatan Literasi Digital melalui Pemanfaatan Aplikasi dan Situs Web Pembelajaran bagi Guru dan Siswa di SDN 1 Guntur Macan Henni Comala Hikmi; Karina Nurwijayanti; Lalu Moh. Nurkholis; Emi Suryadi
SAFARI :Jurnal Pengabdian Masyarakat Indonesia Vol. 5 No. 3 (2025): Juli : SAFARI :Jurnal Pengabdian Masyarakat Indonesia
Publisher : BADAN PENERBIT STIEPARI PRESS

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

Abstract

Digital literacy is a crucial effort in preparing for the Society 5.0 era. By integrating technology with digital literacy, teachers are better equipped to deliver engaging, effective, and high-quality instruction to students who are growing up in a digital-centric environment. Digital literacy empowers students to participate actively in the learning process while fostering critical and creative thinking in utilizing available information. This Community Service aims to enhance the digital literacy of both teachers and students through the introduction and practical application of educational websites and digital tools. The program was implemented in collaboration with SDN 1 Guntur Macan, located in Gunung Sari, West Lombok. The activity involved 9 elementary school teachers and thirty students from grades V and VI. The methodology in this Community Service program consisted of interactive training sessions and hands-on practice with various digital platforms and applications. The program evaluation indicated a notable improvement in teachers' understanding of digital literacy concepts and the use of digital tools related to instruction. Furthermore, the initiative successfully increased teachers' motivation to integrate technology into classroom learning practices.
FAKE REVIEW DETECTION ON DIGITAL PLATFORMS USING THE ROBERTA MODEL: A DEEP LEARNING AND NLP APPROACH Hadi, Zulpan; Nurkholis, Lalu Moh.; Imran, Bahtiar; Riadi, Selamet; Suryadi, Emi
Journal Computer and Technology Vol. 3 No. 1 (2025): Juli 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v3i1.355

Abstract

Fake reviews have emerged as a serious threat to the integrity of digital platforms, particularly in e-commerce and online review sites. This study explores the application of RoBERTa (Robustly Optimized BERT Approach), a transformer-based architecture optimized for natural language processing (NLP), in automatically detecting fake reviews. The methodology includes data collection from online platforms, contextual feature extraction using RoBERTa embeddings, model training through supervised learning, and evaluation using classification metrics such as accuracy, precision, recall, and F1-score. The training results indicate a significant convergence trend in the training loss, while the validation loss remains relatively unstable, reflecting challenges in model generalization. Nevertheless, experimental results demonstrate that RoBERTa outperforms other approaches such as Logistic Regression PU, K-NN with EM, and LDA-BPTextCNN, achieving an accuracy of 86.25%. These findings highlight RoBERTa's strong potential in detecting manipulative content and underscore its value as an essential tool in building a transparent and trustworthy digital ecosystem.
SemetonBug: A Machine Learning Model for Automatic Bug Detection in Python Code Based on Syntactic Analysis Imran, Bahtiar; Riadi, Selamet; Suryadi, Emi; Zulpahmi, M.; Zaeniah, Zaeniah; Wahyudi, Erfan
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.25340

Abstract

Bug detection in Python programming is a crucial aspect of software development. This study develops an automated bug detection system using feature extraction based on Abstract Syntax Tree (AST) and a Random Forest Classifier model. The dataset consists of 100 manually classified bugged files and 100 non-bugged files. The model is trained using structural code features such as the number of functions, classes, variables, conditions, and exception handling. Evaluation results indicate an accuracy of 86.67%, with balanced precision and recall across both classes. Confusion matrix analysis identifies the presence of false positives and false negatives, albeit in relatively low numbers. The accuracy curve suggests a potential overfitting issue, as training accuracy is higher than testing accuracy. This study demonstrates that the combination of AST-based feature extraction and Random Forest can be an effective approach for automated bug detection, with potential improvements through model optimization and a larger dataset.
Co-Authors Ahmad Yani ahmad yani Ahmad Yani Akbar, Ardiyallah Ardiyallah Akbar Ardiyallah Akbar Arwidiyarti, Dwinita Aspari, Roni Astutik Zaerani Aulia, Asma Ayu Awanda Aerin Maesyarani Beni Ari Hidayatulloh Beni Ary Hidayatullah Buchtami, Lalu Reza Dedi Supriadi Diki Hananta Firdaus Efendi, Muhamad Masjun Ema Zulfaningsih Erfan Wahyudi Fadlun Mudin Giardi, Muh Hamzah Andung Hamzan Wadi Henni Comala Hikmi Imran, Bahtiar Juniyardi, Lalu Karim, Muh Nasirudin Karina Nurwijayanti Karina Nurwijayanti Lalu Darmawan Bakti Lalu Delsi Samsumar, M.Eng. Lalu Efendi Novian Lalu Moh. Nurkholis M. Zulpahmi Maspaeni Maspaeni Moh Subli Moh. Subli Mudin, Fadlun Nining Putri Ningsih Novian, Lalu Efendi NUR FAUZIAH Nurkholis, Lalu Moh. Nurul Yakin Nurul Yakin Nurul Yakin Rudi Muslim Rudi Muslim Said, Syukroni Selamet Riadi Selamet Riadi Subli Kerta Yan Santosa Yumarlin MZ Zaeniah Zaeniah Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zulpahmi, M. Zulpan Hadi