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All Journal Jurnal Teknologi Sistem Informasi dan Aplikasi IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences International Journal for Applied Information Management JAMAIKA: Jurnal Abdi Masyarakat KOMMAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Bhinneka Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia Kohesi: Jurnal Sains dan Teknologi Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA)
Sofiana, Sofa
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Perancangan Sistem Informasi Rekam Medis Berbasis Web (Studi Kasus: Klinik CAS Medica) Nurulita, Fitri; Sofiana, Sofa
Buletin Ilmiah Ilmu Komputer dan Multimedia Vol 1 No 2 (2023): Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA)
Publisher : Shofanah Media Berkah

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Abstract

Bagian staff dalam melakukan pengolahan data obat kerja terdapat banyak katalog yang tidak tersusun dengan baik, sehingga sulitnya bagian staff dalam melakukan pencarian data obat, staff gudang harus mengecek data satu persatu untuk melakukan pencarian data obat. Staff dalam menjumlah data-data obat harus memasukan rumus sum adalah fungsi excel yang digunakan untuk melakukan penjumlahan data terlebih dahulu mengakibatkan lamanya data penjumlahan total obat dan sering keliruan peletakan cell menimbulkan perhitungan total obat tidak sesuai dengan fisiknya. Dalam pengecekan stock obat masih menggunakan proses manual dengan tulis tangan pada kartu stock dan sering terjadi kesalahan dalam penulisan kuantiti dan harga sehingga mengakibatkan stock yang tidak sesuai. Ketika melihat stock staff menghitung atau mengakumulasi keseluruhan transaksi antara obat masuk, obat keluar sehingga membutuhkan waktu yang cukup lama dalam pencarian stock. Oleh karena itu peneliti mengambil tema dengan judul “Perancangan Sistem Informasi Rekam Medis Berbasis Web (Studi Kasus: Klinik Cas Medika)untuk menyelesaikan permasalahan yang terjadi pada Klinik Cas Medica.
An Extensive Exploration into the Multifaceted Sentiments Expressed by Users of the myIM3 Mobile Application, Unveiling Complex Emotional Landscapes and Insights Hayadi, B Herawan; Henderi, Henderi; Budiarto, Mukti; Sofiana, Sofa; Padeli, Padeli; Setiyadi, Didik; Swastika, Rulin; Arifin, Rita Wahyu
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.187

Abstract

This study investigates user sentiment towards the myIM3 application, an application used for telecommunication service management in Indonesia. Using text analysis and machine learning methods, we analyzed user reviews to identify dominant sentiment patterns and evaluate different classification models. Word cloud analysis, sentiment distribution, and donut plots were utilized to gain deeper insights into user preferences and issues. Results indicate that the majority of user reviews are neutral (52.2%), with 37% positive reviews and 33.4% negative reviews. Users consistently pay attention to aspects such as internet connection (Neutral: 92%, Positive: 95%, Negative: 87%) and pricing (Neutral: 92%, Positive: 92%, Negative: 93%) in their reviews. Evaluation of classification models like Decision Tree Classifier, Support Vector Machine (SVM), and Random Forest shows that the SVM model performs the best with an accuracy of 93%, high precision (Negative: 93%, Neutral: 92%, Positive: 95%), recall (Negative: 93%, Neutral: 95%, Positive: 91%), and F1-score (Negative: 93%, Neutral: 94%, Positive: 93%). These findings can serve as a basis for service improvement and better product development in the future, while also affirming the capability of text analysis and machine learning techniques in providing valuable insights for telecommunication service providers.
Unsupervised Learning Methods for Topic Extraction and Modeling in Large-scale Text Corpora using LSA and LDA Henderi, Henderi; Hayadi, B Herawan; Sofiana, Sofa; Padeli, Padeli; Setiyadi, Didik
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.102

Abstract

This research compares unsupervised learning methods in topic extraction and modeling in large-scale text corpora. The methods used are Singular Value Decomposition (SVD) and Latent Dirichlet Allocation (LDA). SVD is used to extract important features through term-document matrix decomposition, while LDA identifies hidden topics based on the probability distribution of words. The research involves data collection, data exploratory analysis (EDA), topic extraction using SVD, data preprocessing, and topic extraction using LDA. The data used were large-scale text corpora. Data explorative analysis was conducted to understand the characteristics and structure of text corpora before topic extraction was performed. SVD and LDA were used to identify the main topics in the text corpora. The results showed that SVD and LDA were successful in topic extraction and modeling of large-scale text corpora. SVD reveals cohesive patterns and thematically related topics. LDA identifies hidden topics based on the probability distribution of words. These findings have important implications in text processing and analysis. The resulting topic representations can be used for information mining, document categorization, and more in-depth text analysis. The use of SVD and LDA in topic extraction and modeling of large-scale text corpora provides valuable insights in text analysis. However, this research has limitations. The success of the methods depends on the quality and representativeness of the text corpora. Topic interpretation still requires further understanding and analysis. Future research can develop methods and techniques to improve the accuracy and efficiency of topic extraction and text corpora modeling.
PENGENALAN KONTEN DIGITAL MARKETING MENGUNAKAN ARTIFICIAL INTELLIGENT PADA SMK NUSANTARA CIPUTAT octaviano, alvino; Sofiana, Sofa; Santoso, Bambang
KOMMAS: Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 1 (2024): KOMMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : KOMMAS: Jurnal Pengabdian Kepada Masyarakat

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Abstract

Perkembangan teknologi digital dan kecerdasan buatan (AI) telah membawa perubahan signifikan dalam dunia pemasaran. Untuk mempersiapkan siswa SMK menghadapi tuntutan industri di era digital, diperlukan pengenalan terhadap penggunaan AI dalam digital marketing. Program Pengabdian Kepada Masyarakat (PKM) ini bertujuan untuk memperkenalkan konsep dan aplikasi AI dalam pembuatan konten digital marketing kepada siswa SMK Nusantara Ciputat. Metode yang digunakan meliputi pelatihan, workshop, dan praktik langsung penggunaan tools AI untuk digital marketing. Materi yang disampaikan mencakup pengenalan dasar AI, pemanfaatan AI dalam content creation, copywriting, dan optimalisasi strategi pemasaran digital. Hasil yang diharapkan adalah peningkatan pemahaman dan keterampilan siswa dalam memanfaatkan teknologi AI untuk menciptakan konten digital marketing yang efektif dan relevan dengan kebutuhan industri. Program ini juga bertujuan untuk meningkatkan daya saing lulusan SMK di bidang digital marketing dan mendorong pengembangan kompetensi yang selaras dengan perkembangan teknologi terkini.
IMPLEMENTASI APLIKASI GAMES EDUKASI DENGAN BAHASA ISYARAT SIBI UNTUK ANAK USIA 5 SAMPAI 9 TAHUN DENGAN HAMBATAN TUNARUNGU BERBASIS ANDROID Erminah, Erminah; Sofiana, Sofa
Kohesi: Jurnal Sains dan Teknologi Vol. 7 No. 7 (2025): Kohesi: Jurnal Sains dan Teknologi
Publisher : CV SWA Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3785/kohesi.v7i7.11960

Abstract

Problems that occur in children aged 5 to 9 years with hearing impairments in recognizing and learning the alphabet, where it is very difficult to find learning media, both books and electronic media, that can be used by children aged 5 to 9 years with hearing impairments. Introduction to the alphabet is the basic key. so that children can read. The library study method is a data collection technique by examining, studying and comparing existing theories or research results, whether from books, journals or other media related to the research being carried out. Observation Method is a method carried out by systematically observing and recording a research subject directly in the field, intended to obtain data naturally. Interviews attempt to obtain information by asking informants directly. The use of this application provides good facilities in the form of educational games and can be applied as a practical and interesting SIBI teaching tool as well as implementing educational applications as learning aids. Implementation of an educational games application using sign language for children aged 5 to 9 years with hearing impairments based on Android makes it easy for educators and students. Based on the descriptions explained in the previous chapters, it can be concluded that this Android-based application provides information about the introduction of SIBI numbers and letters for the hearing impaired. It can be proven from the results obtained in the questionnaire that 85.6% strongly agreed, this shows that the application made was delivered on target
Comparative Study of Traditional and Modern Models in Time Series Forecasting for Inflation Prediction Henderi, Henderi; Sofiana, Sofa
International Journal for Applied Information Management Vol. 5 No. 3 (2025): Regular Issue: September 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i3.108

Abstract

Time series forecasting plays a crucial role in economic analysis, particularly in anticipating inflation and policy planning. This study compares the performance of seven different time series forecasting models, namely ARIMA, SARIMA, ETS, Prophet, LSTM, XGBoost, and TCN, in predicting inflation rates. Each model was applied to four years of inflation data to test its accuracy and reliability. The evaluation was conducted using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) to measure the performance of each model. The results indicate that deep learning models, particularly LSTM and TCN, achieved the highest accuracy with the lowest MSE and RMSE values, specifically 0.0008 and 0.0015 for LSTM, and 0.0007 and 0.0013 for TCN, indicating their capability in capturing complex temporal patterns. Traditional models such as ARIMA and SARIMA, while effective in capturing trends and seasonality, showed limitations in handling non-linear patterns and sudden changes, with MSE and RMSE values of 0.0012 and 0.0024 for ARIMA, and 0.0011 and 0.0023 for SARIMA, respectively. ETS, with the highest MSE and RMSE values of 0.0013 and 0.0025, demonstrated limitations in dealing with the complexity of inflation data. XGBoost also showed good performance with MSE and RMSE values of 0.0009 and 0.0018, combining flexibility and robustness in handling complex data. Prophet achieved an MSE of 0.0010 and RMSE of 0.0020, indicating that while it effectively captures seasonal trends, there is room for improvement in handling rapid inflation increases. This research provides in-depth insights into the strengths and weaknesses of each model, as well as recommendations for practical applications in inflation forecasting. By presenting a comprehensive comparative analysis, this study aims to assist researchers and practitioners in selecting the most suitable forecasting model for their specific needs
A Machine Learning Approach to Indonesian Climate Change Sentiment Analysis Using Naive Bayes Henderi, Henderi; Sofiana, Sofa
International Journal of Informatics and Information Systems Vol 8, No 1: January 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i1.246

Abstract

Climate change poses a significant global challenge, particularly for archipelagic nations such as Indonesia that are highly vulnerable to rising temperatures and extreme weather events. This study applies machine learning-based sentiment analysis to assess Indonesian public opinion on climate change using Twitter data. A total of 5,120 Indonesian-language tweets were collected through keyword-based scraping related to climate and weather conditions. Following text preprocessing (lowercasing, stopword removal, stemming, and cleaning), TF-IDF vectorization was used to extract the top 400 most significant terms. The dataset was divided into training (80%) and testing (20%) subsets, and a Multinomial Naïve Bayes classifier was trained to categorize sentiments into positive, neutral, and negative classes. The results show a dominance of negative sentiment (62%), primarily associated with extreme heat and storm-related events, while neutral (24%) and positive (14%) sentiments were linked to moderate weather conditions. Model evaluation achieved an F1-score of 0.95 for negative, 0.86 for neutral, and 0.83 for positive sentiment, yielding a macro-average F1-score of 0.88. The analysis also identified “panas (hot),” “hujan (rain),” and “banjir (flood)” as top lexical indicators influencing classification. Overall, the findings highlight that Indonesian public sentiment toward climate change is highly reactive to extreme weather. The study underscores the potential of Naïve Bayes as a baseline model for real-time environmental sentiment monitoring, offering valuable insights for institutions such as BMKG to enhance public communication and climate awareness strategies.
PENINGKATAN KETERAMPILAN SISWA SMK DALAM PENGEMBANGAN PRODUK DIGITAL BERBASIS AI: PENGEMBANGAN PRODUK DIGITAL BERBASIS AI Octaviano, Alvino; Sofiana, Sofa; Santoso, Bambang
KOMMAS: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 2 (2025): KOMMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : KOMMAS: Jurnal Pengabdian Kepada Masyarakat

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Abstract

Banyak siswa SMK masih kurang pemahaman dasar tentang kecerdasan buatan atau Artificial Intelligence (AI) dan aplikasinya dalam dunia digital (Haryoko et al., 2024). Kurikulum SMK belum sepenuhnya mengakomodasi perkembangan AI, sehingga tidak selaras dengan kebutuhan industri (Octaviano et al., 2024). Keterbatasan fasilitas dan infrastruktur menghambat pembelajaran berbasis AI di sekolah (Firdiansyah, 2025). Di sisi lain, kompetensi guru dalam mengajar AI masih terbatas, memerlukan pelatihan intensif (Sudaryatno, 2025). Kurangnya pelatihan praktis juga mengurangi kemampuan siswa dalam mengimplementasikan AI (Darwis et al., 2024). Pengembangan soft skills seperti komunikasi dan problem-solving diperlukan untuk mendukung kolaborasi dalam proyek AI. Perlu adaptasi terhadap perubahan teknologi agar siswa dapat mengikuti tren industri (Microsoft.com, 2024). Penelitian ini menawarkan solusi holistik melalui pembaruan kurikulum, pelatihan guru, kolaborasi industri, dan pembelajaran berbasis proyek.
Aplikasi Presensi Siswa Dan Guru Di SMK AN NUR Depok Menggunakan QR Code Berbasis Android Irwansyah; Sofiana, Sofa
BINER : Jurnal Ilmu Komputer, Teknik dan Multimedia Vol. 1 No. 4 (2023): BINER : Jurnal Ilmu Komputer, Teknik dan Multimedia
Publisher : CV. Shofanah Media Berkah

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Abstract

As a result of technological advances, especially in the world of informatics and the increasing number of emerging software created to overcome the problem of human error. In life, the system plays an important role so that what is needed should be obtained quickly, accurately and easily. Writing and the desire to design this attendance application so that the attendance process is all done in a computerized system. Therefore the researcher got the idea to solve the problem by creating an Android-based system that can connect directly to the school's server. This system is equipped with a QR Code to reduce abuse and manipulation of attendance by teachers and staff. In this study, researchers used Kodular to create an Android-based presence system, and presence reports can be printed from Google Spreadsheet. The hope of this research is that an Android-based attendance system with the QR Code feature can run well and meet the needs of the AN NUR Vocational School.
Perancangan Sistem Informasi Absensi Asisten Lab Berbasis Android pada Universitas Pamulang Octaviano, Alvino; Sofiana, Sofa; Priyadi, Satrio Pandita
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 3 No. 4 (2020): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

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Abstract

Fingerprint recognition with distance-based optimization and pattern matching, "international conference on signal processing, communication, power, and data management systems on attendance paper makes it very difficult to log absent and absent assistants. In this case Change of attendance records to save. in a digital-based information system database. Meanwhile, fingerprint attendance methods or methods have been used on cellphones. Record attendance. Method makes applications that support digital attendance with Android. Android is an operating system application for mobile phones based on Linux. Android offers developers an open platform where they can create their own applications used by various mobile devices.
Co-Authors Ahmad Burhanuddin, Ahmad Aisyah Aisyah Anugrah, Anggita Dwi Arifin, Rita Wahyu B. Herawan Hayadi Bambang Santoso Budiarto, Mukti Didik Setiyadi Erminah, Erminah Fachri Ali Henderi . Imam Prayogo Pujiono IRWANSYAH Nawan, Agus Nurulita, Fitri Octaviano, Alvino Padeli Padeli Priyadi, Satrio Pandita rahayu, eka puji Saizi, Muhammad Salman Swastika, Rulin Zuhrufillah, Khafifah