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All Journal Jurnal Algoritma Jurnal Ilmiah Sinus Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Pendidikan Indonesia (Japendi)
Shiddieq, Diqy Fakhrun
Unknown Affiliation
Computer Science & IT Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

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Perancangan Sistem Informasi Bimbingan Skripsi Online (SIBIMO) dengan SCRUM Framework Roji, Fikri Fahru; Shiddieq, Diqy Fakhrun; Gusdiana, Ridian; Puspita, Evi
Jurnal Algoritma Vol 20 No 2 (2023): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.20-2.1459

Abstract

The development of technology and the internet has opened up opportunities for advances in information systems. In this context, the Online Thesis Guidance Information System (SIBIMO) is an efficient solution in the thesis guidance process in higher education. With the SCRUM method, SIBIMO allows students to submit thesis titles, interact with supervisors, and manage seminar and trial stages through a designed user interface. This methodology combines stages such as UI design, functionality testing, and progress evaluation. Testing each SIBIMO menu ensures the system is running as intended. SCRUM allows achieving development time targets and efficient adaptation at each iteration. This research concludes that SIBIMO, which was developed using the SCRUM method, was successful in designing an Information System that was carried out effectively and efficiently in managing the Online Tutoring Information System design process.
Perancangan UI/UX Sistem Informasi Akademik Menggunakan Pendekatan Design Thinking Shiddieq, Diqy Fakhrun; Nurhayati, Dwi
Jurnal Ilmiah SINUS Vol 23, No 1 (2025): Vol. 23 No. 1, Januari 2025
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v23i1.862

Abstract

The significant shift in the utilization of technology has led most universities worldwide to adopt various digital platforms. Academic information systems represent one such technological implementation that plays a crucial role in the retention and success of academic and non-academic activities within higher education institutions. Although many institutions, including Garut University, have developed academic information systems, UI/UX remain significant issues. Suboptimal interface design can cause user frustration, reduce productivity, decrease cognitive performance, affect users' emotional responses, and even lower the technology adoption rate. This research was conducted to provide system recommendations by analyzing and designing the UI/UX of the academic information system at Garut University using the design thinking method. The recommendations are presented as prototypes developed using the Figma. The prototypes were tested with 30 respondents using the HEART Framework, presented in a goal-signal-metric format, with the highest or strongly agreed responses recorded for all variables: happiness at 58.89%, engagement at 57.78%, adoption at 76.67%, retention at 66.67%, and task success at 61.11%. These results indicate that the adoption variable obtained the highest score, suggesting that the information within the academic information system meets user needs, is easy to learn, and that the system quality is rated as good.
Penggunaan Multivariat Model Bidirectional LSTM untuk Prediksi Cuaca: Optimalisasi Waktu Tanam Padi Petani Kabupaten Garut Aswarulloh, Haris; Shiddieq, Diqy Fakhrun; Nurhayati, Dwi
Jurnal Ilmiah SINUS Vol 23, No 1 (2025): Vol. 23 No. 1, Januari 2025
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v23i1.891

Abstract

The unpredictability of climate conditions poses significant challenges for agricultural activities, particularly in Garut Regency, where traditional knowledge often guides planting schedules. This study aims to optimize rice planting schedules by employing a Bidirectional Long Short-Term Memory (BiLSTM) model for multivariate time series forecasting of weather parameters. The research utilized meteorological data from BMKG, including average temperature, relative humidity, rainfall, and sunshine duration, which were preprocessed to ensure data quality and normalized for modeling. The BiLSTM model demonstrated superior performance in predicting key variables such as temperature and humidity, achieving low Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). However, higher errors were observed for rainfall and sunshine duration due to the complex nature of these variables. The study successfully identified optimal planting periods by aligning weather predictions with criteria for rice cultivation, providing a comprehensive calendar to assist farmers. These findings emphasize the potential of advanced machine learning models in mitigating climate-related agricultural risks and improving productivity. Future studies may focus on integrating additional meteorological factors to enhance prediction accuracy.
Penerapan Algoritma Naive Bayes dengan Teknik TF-IDF dan Cross Validation untuk Analisis Sentimen Terhadap Starlink: Application of the Naive Bayes Algorithm with TF-IDF and Cross Validation Techniques for Sentiment Analysis Towards Starlink Khoerunnisa, Suci; Shiddieq, Diqy Fakhrun; Nurhayati, Dwi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1852

Abstract

Starlink, layanan internet satelit dari SpaceX, mulai beroperasidi Indonesia pada 2024 untuk mengatasi kesenjangan digital di wilayah terpencil. Namun, kehadirannya menimbulkan tantangan seperti harga tinggi, potensi dampak terhadap penyedia lokal, dan masalah regulasi. Penelitian ini mengkaji sentimen publik terhadap Starlink menggunakan algoritma Naïve Bayes yang dikombinasikan dengan teknik TF-IDF dan Cross Validation yang masih jarang diterapkan dalam studi serupa di Indonesia. Data yang digunakan berupa cuitan berbahasa Indonesia dari pengguna platform X selama Mei-November 2024. Hasil analisis menunjukkan bahwa model Naïve Bayes memiliki kinerja optimal dalam mendeteksi sentimen positif dibandingkan negatif maupun netral, sebagaimana diukur menggunakan confusion matrix. Temuan utama menunjukkan bahwa Naïve Bayes 49,38% cuitan bersentimen positif, 32,94% netral, dan 17,68% negatif. Sentimen positif didominasi oleh apresiasi terhadap kecepatan dan stabilitas layanan, sedangkan sentimen negatif mengkritik harga tinggi dan dampaknya terhadap penyedia lokal. Meskipun model menunjukkan performa baik pada sentimen positif, akurasi klasifikasi sentimen negatif dan netral masih perlu ditingkatkan. Hasil penelitian ini memberikan wawasan strategis bagi pengembangan bisnis Starlink serta dasar pertimbangan bagi pemerintah terkait layanan internet berbasis satelit di Indonesia.
Analisis Sentimen Publik X Terhadap Kenaikkan Ppn 12% pada Twitter Menggunakan Latient Dirichlet Allocation Nulhakim, Rizki Fajar; Shiddieq, Diqy Fakhrun
Jurnal Pendidikan Indonesia Vol. 6 No. 8 (2025): Jurnal Pendidikan Indonesia
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/japendi.v6i8.8544

Abstract

Rencana kenaikan tarif Pajak Pertambahan Nilai (PPN) menjadi 12% pada tahun 2025 telah menjadi salah satu isu kebijakan publik yang hangat diperbincangkan oleh masyarakat. Kebijakan ini memunculkan berbagai reaksi, baik dukungan maupun penolakan, terutama di media sosial X (sebelumnya Twitter), yang menjadi wadah dinamis bagi masyarakat dalam menyuarakan opini. Penelitian ini bertujuan untuk menganalisis persepsi publik terhadap rencana kenaikan PPN tersebut dengan menggunakan pendekatan analisis topik dan sentimen. Metode yang digunakan adalah Latent Dirichlet Allocation (LDA), yang memungkinkan identifikasi tema-tema dominan dalam percakapan daring. Data dikumpulkan secara otomatis menggunakan Twitter Harvest dengan kata kunci “PPN 12%” dalam kurun waktu Juli hingga Oktober 2024. Data mentah kemudian melalui tahapan preprocessing sebelum dilakukan pemodelan topik. Hasil analisis menunjukkan bahwa terdapat tujuh topik utama yang muncul dari percakapan publik, meliputi isu-isu seperti daya beli masyarakat, efektivitas kebijakan pemerintah, sistem perpajakan, serta dampak ekonomi. Nilai coherence score tertinggi yang diperoleh adalah sebesar 0,88, yang menunjukkan bahwa model topik yang dihasilkan memiliki kualitas yang baik. Temuan ini memperlihatkan bahwa metode LDA mampu secara efektif menggambarkan opini dan persepsi publik terhadap isu kebijakan. Hasil penelitian ini diharapkan dapat menjadi bahan pertimbangan bagi pembuat kebijakan dalam merancang strategi yang lebih responsif terhadap aspirasi dan kekhawatiran masyarakat luas. Abstract The plan to increase the Value Added Tax (VAT) rate to 12% by 2025 has become one of the public policy issues that is hotly discussed by the public. This policy has generated various reactions, both support and rejection, especially on social media X (formerly Twitter), which has become a dynamic forum for the public to voice their opinions. This study aims to analyze public perception of the VAT increase plan using a topic and sentiment analysis approach. The method used is Latent Dirichlet Allocation (LDA), which allows the identification of dominant themes in online conversations. The data was collected automatically using Twitter Harvest with the keyword "12% VAT" in the period from July to October 2024. The raw data then goes through a preprocessing stage before topic modeling is carried out. The results of the analysis show that there are seven main topics that emerge from the public conversation, including issues such as people's purchasing power, the effectiveness of government policies, the tax system, and economic impacts. The highest coherence score obtained was 0.88, which indicates that the resulting topic model is of good quality. These findings show that the LDA method is able to effectively describe public opinion and perception of policy issues. The results of this research are expected to be considered for policymakers in designing strategies that are more responsive to the aspirations and concerns of the wider community.
Co-Authors Aswarulloh, Haris Dwi Nurhayati, Dwi Fikri Fahru Roji Khoerunnisa, Suci Nulhakim, Rizki Fajar Puspita, Evi Ridian Gusdiana