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All Journal SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JOURNAL OF SCIENCE AND SOCIAL RESEARCH JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE)
Sembiring, Asha
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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ANALISIS DATA GOVERNANCE DOMAIN DATA QUALITY MENGGUNAKAN DAMA-DMBOKV2 (DINAS KOMUNIKASI DAN INFORMATIKA KOTA BANDUNG) Alhari, Muhammad Ilham; Salsabilla, Annisa Nur; Sembiring, Asha
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 3 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i3.5004

Abstract

Data sekarang sudah menjadi bagian penting bagi perusahaan atau organisasi, mulai dari ilmu, bahkan sampai menjadi acuan dalam pengambilan keputusan dan strategi bisnis perusahaan. Data dan informasi bukan hanya aset yang diharapkan dapat meningkat nilainya dimasa depan. Melainkan data juga merupakan objek vital bagi setiap organisasi. Data memiliki tiga sifat wajib, yaitu integrity, availability, confidentiality. Ketiga sifat itu harus ada karena jika tidak, maka data tersebut dapat dikatakan tidak valid. Kualitas data juga merupakan hal mutlak yang harus dijaga agar output dari data tersebut, yaitu informasi, dapat menjadi informasi yang berkualitas. Setiap perusahaan, organisasi, ataupun instansi pasti memiliki cara masing-masing dalam mengelola dan menjaga kualitas data mereka. Untuk itu diperlukan standar pengelolaan kualitas yang baik. Data quality management membantu dalam mengelola dan menjaga kualitas data dengan standar operasional yang ada. Penelitian ini berfokus dalam analisis dan penilaian terhadap tata kelola kualitas data di Dinas Komunikasi dan Informatika Kota Bandung dengan berpedoman pada kerangka kerja DAMA-DMBOKv2. Analisis dilakukan untuk membandingkan antara kondisi existing dan targeting yang ingin dicapai. Hasil dari penelitian ini berupa rekomendasi berdasarkan GAP Analysis terhadap proses data quality management untuk mengembangkan standar pengelolaan kualitas data agar mencapai tujuan yang diinginkan.
Designing a Female Hero Educational Game using Adobe Animate and the ADDIE Method Sembiring, Asha; Sagala, Alon Jala Tirta; Yahaya, Wan Ahmad Jaafar Wan
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i1.18325

Abstract

Many do not know that heroes in Indonesia are not only men but women who also participate in the struggle and act as heroes in Indonesia. Female heroes are also very instrumental for Nusa and the Nation. Technology development is now increasingly advanced. For example, mobile phones are owned by every group, such as children, teenagers to the elderly. Mobile phones can also be used as entertainment media such as games that can increase children’s interest in learning who tend to like animated images and can increase knowledge, so education is not too saturated. This study aims to develop an educational game that can introduce national female heroes using the ADDIE (Analysis, Design, Development, Implementation, Evaluation) method. This development method focuses on iteration and reflection, so continuous improvement can be made that focuses on feedback. Using a questionnaire, software testing techniques focused on functional specifications and usability testing on a Likert scale. Blackbox testing can provide an overview of the program combined state and perform functional testing. Likert scale questionnaire testing can produce as expected. The result of the research is an educational game introducing the heroine, which is expected to be used to introduce the national heroine to children and students.
PERBANDINGAN PREDIKSI POLUSI UDARA MENGGUNAKAN LSTM DAN BILSTM Pratama, Andre; Sembiring, Asha; Nababan, Junerdi; Zarkasyi, Muhammad Imam; Rahayu, Novriza
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3596

Abstract

Abstract: Air pollution has become a serious problem in densely populated urban areas such as DKI Jakarta. To mitigate its negative impacts, an accurate air pollution prediction system is necessary. This study compares the performance of two deep learning models, Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM), in predicting PM10 concentration using air quality data from DKI Jakarta between 2016 and 2019. The research process includes data collection and preprocessing, model training, and model evaluation. Both models were tested with various parameters such as the number of hidden neurons, dropout rate, epochs, and batch size. The results consistently show that BiLSTM outperforms LSTM, achieving lower Root Mean Square Error (RMSE) values across 54 testing scenarios. The best BiLSTM configuration, with 64 hidden neurons, 0.2 dropout rate, 50 epochs, and batch size 16, yielded an RMSE of 9.311401. Meanwhile, the best LSTM configuration, with 128 hidden neurons, 0.1 dropout rate, 100 epochs, and batch size 16, produced an RMSE of 9.330554. The advantage of BiLSTM lies in its ability to process data bidirectionally, making it more effective in capturing temporal patterns for air pollution prediction compared to LSTM. Keywords: air pollution prediction, pollutant, deep learning, LSTM, BiLSTM Abstrak: Pencemaran udara menjadi masalah serius di wilayah perkotaan padat seperti DKI Jakarta. Untuk mengurangi dampak negatifnya, diperlukan sistem prediksi polusi udara yang akurat. Penelitian ini membandingkan performa dua model deep learning, Long Short-Term Memory (LSTM) dan Bidirectional Long Short-Term Memory (BiLSTM), dalam memprediksi konsentrasi PM10 menggunakan data kualitas udara DKI Jakarta tahun 2016-2019. Proses penelitian mencakup pengumpulan dan praproses data, pelatihan model, serta evaluasi model. Kedua model diuji dengan berbagai parameter seperti jumlah hidden neuron, dropout rate, epoch, dan batch size. Hasil menunjukkan BiLSTM lebih unggul secara konsisten dengan nilai Root Mean Square Error (RMSE) lebih rendah melalui 54 skenario pengujian. Konfigurasi terbaik BiLSTM menggunakan 64 hidden neuron, dropout rate 0.2, 50 epoch, dan batch size 16 menghasilkan RMSE 9.311401. Sedangkan konfigurasi LSTM terbaik pada 128 hidden neuron, dropout rate 0.1, 100 epoch, dan batch size 16 menghasilkan RMSE 9.330554. Keunggulan BiLSTM terletak pada kemampuannya memproses data dua arah, sehingga lebih efektif dalam menangkap pola temporal untuk prediksi polusi udara dibandingkan LSTM.  Kata kunci: prediksi polusi udara, polutan, deep learning, LSTM, BiLSTM
ANALISIS SENTIMEN PENGGUNA INSTAGRAM TERHADAP STATEMENT MENTERI KEUANGAN TENTANG “KEBIJAKAN GAJI GURU DAN DOSEN” MENGGUNAKAN NAÏVE BAYES Santoso, M. Imam; Mardiah, Nia; Sembiring, Asha
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4385

Abstract

Abstract: The Minister of Finance's statement regarding the teacher and lecturer salary policy sparked mixed public reactions, particularly on social media. This study aims to analyze the sentiment of the Indonesian public, particularly Instagram users, regarding this statement using the Nae Bayes algorithm. Data was collected through scraping using Instant Data Scrapper in August 2025, totaling 939 data points. Preprocessing steps included cleansing, tokenizing, stopword removal, and stemming. The data were then classified into two sentiment categories: positive and negative. The results showed that the majority of Instagram user sentiment tended to be negative (859 data points, representing 91.5%), while positive sentiment accounted for 89 data points, representing 9.5%. The Nae Bayes model achieved an accuracy of 0.87 in classifying public opinion. These findings indicate that the Nae Bayes algorithm is effective in analyzing public opinion on sensitive issues on social media. Furthermore, the results of this study can serve as a reference for the government and policymakers in understanding public perception and formulating more appropriate communication strategies related to education policy. Keyword: Sentiment Analysis, Nae Bayes, Minister of Finance, Instagram, Text Analysis. Abstrak: Pernyataan Menteri Keuangan tentang kebijakan gaji guru dan dosen memicu beragam reaksi publik, khususnya di media sosial. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat Indonesia, terutama pengguna Instagram, terhadap pernyataan tersebut dengan menggunakan algoritma Nae Bayes. Data dikumpulkan melalui proses scraping menggunakan Instant Data Scrapper pada Agustus 2025 dengan jumlah 939 data. Kemudian dilakukan tahap praproses meliputi cleansing, tokenizing, stopword removal, dan stemming. Selanjutnya, data diklasifikasikan ke dalam dua kategori sentimen, yaitu positif dan negatif. Hasil penelitian menunjukkan bahwa mayoritas sentimen pengguna Instagram cenderung Negatif (859 data dengan persentase 91,5%), sedangkan sentimen positif berjumlah 89 data dengan persentase 9,5%. Model Nae Bayes mencapai tingkat akurasi sebesar 0,87 dalam mengklasifikasikan opini publik. Temuan ini mengindikasikan bahwa algoritma Nae Bayes efektif dalam menganalisis opini publik pada isu sensitif di media sosial. Selain itu, hasil penelitian ini dapat menjadi acuan bagi pemerintah dan pemangku kebijakan dalam memahami persepsi publik serta merumuskan strategi komunikasi yang lebih tepat terkait kebijakan pendidikan. Kata kunci: Sentimen Analisis, Nae Bayes, Menteri Keuangan, Instagram, Analisis Teks.
Optimalisasi skill digital marketing berbasis ICT untuk siswa SMK jurusan akuntansi dan perkantoran Nababan, Junerdi; Sembiring, Asha; Hazimah, Zulfa; Kusnadi, Kusnadi; Ravel, Mutiha Aristo
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 6 (2025): November (In Progress)
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v9i6.34283

Abstract

Abstrak Perkembangan teknologi digital menuntut peningkatan keterampilan digital marketing yang berbasis teknologi informasi bagi siswa SMK, khususnya jurusan Akuntansi dan Perkantoran. Program pengabdian kepada masyarakat ini bertujuan mengoptimalkan skill digital marketing berbasis ICT untuk siswa SMK Akuntansi dan Perkantoran SMK Negeri 1 Kutalimbaru. Kegiatan ini diikuti oleh 24 siswa sebagai mitra sasaran yang memperoleh pelatihan praktik pembuatan katalog produk digital menggunakan Canva, pembuatan laporan respons kampanye menggunakan Excel dan Google Sheets, analisis data pelanggan dari form pemesanan, serta penjadwalan konten dan follow-up pelanggan. Metode pelaksanaan menggunakan pendekatan Project Based Learning dengan kombinasi ceramah, praktik langsung, diskusi, dan evaluasi. Hasil pelatihan menunjukkan peningkatan kemampuan siswa dalam menguasai alat digital yang relevan dengan kebutuhan industri 5.0, tercermin dari kemampuan mereka membuat materi promosi digital yang profesional dan laporan analisis data kampanye yang informatif. Program ini berkontribusi pada peningkatan daya saing lulusan SMK dalam menghadapi tantangan dunia kerja digital dan mendukung transformasi pendidikan vokasi berbasis ICT. Kata kunci: pengabdian masyarakat; pelatihan; digital marketing; ICT; SMK akuntansi dan perkantoran. Abstract The development of digital technology requires an increase in information technology-based digital marketing skills for vocational school students, especially those majoring in accounting and office administration. This community service program aims to optimize ICT-based digital marketing skills for vocational high school students in the Accounting and Office Administration departments at State Vocational High School 1 Kutalimbaru. The program was attended by 24 students as target participants who received practical training in creating digital product catalogs using Canva, generating campaign response reports using Excel and Google Sheets, analyzing customer data from order forms, and scheduling content and customer follow-ups. The implementation method used a Project-Based Learning approach combined with lectures, hands-on practice, discussions, and evaluations. The training results demonstrated an improvement in students' ability to master digital tools relevant to Industry 5.0 needs, reflected in their ability to create professional digital promotional materials and informative campaign data analysis reports. This program contributes to enhancing the competitiveness of vocational school graduates in facing the challenges of the digital workplace and supports ICT-based vocational education transformation. Keywords: community service; training; digital marketing; ICT; vocational school of accounting and office administration.
Co-Authors Andre Pratama Hazimah, Zulfa Kusnadi Mardiah, Nia Muhammad Ilham Alhari Nababan, Junerdi Rahayu, Novriza Ravel, Mutiha Aristo Sagala, Alon Jala Tirta Salsabilla, Annisa Nur Santoso, M. Imam Yahaya, Wan Ahmad Jaafar Wan Zarkasyi, Muhammad Imam