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All Journal Jurnal Ilmiah Kursor Jurnal Sains Matematika dan Statistika Indonesian Journal of Artificial Intelligence and Data Mining JTAM (Jurnal Teori dan Aplikasi Matematika) JTIK (Jurnal Teknik Informatika Kaputama) JISA (Jurnal Informatika dan Sains) Abdimasku : Jurnal Pengabdian Masyarakat Unnes Journal of Mathematics IJCOSIN : Indonesian Journal of Community Service and Innovation Jurnal Pengabdian Masyarakat dan aplikasi Teknologi Jurnal Pengabdian Sosial
Putri, Aina Latifa Riyana
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Religion 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Optimalisasi AI secara Etis: Strategi Guru Meningkatkan Kualitas Karya Ilmiah untuk Menembus Jurnal Nasional Terakreditasi Puspa, Sofia Debi; Riyono, Joko; Pujiastuti, Christina Eni; Putri, Dianing Novita Nurmala; Putri, Aina Latifa Riyana
Jurnal Pengabdian Masyarakat dan aplikasi Teknologi Vol. 4, No. 2: October 2025
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.adipati.2025.v4i2.7910

Abstract

Penulisan karya ilmiah penting bagi guru untuk mendukung pengembangan ilmu dan peningkatan karir profesionalisme. Namun, guru sering mengalami kendala, seperti sulit menentukan topik, keterbatasan pemahaman struktur penulisan, serta keterbatasan penggunaan manajer referensi yang belum optimal. Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan untuk mengembangkan pengetahuan dan keterampilan guru dalam penulisan karya imiah serta meningkatkan literasi digital dalam pemanfaatan AI, seperti Elicit dan Research Rabbit, dengan tetap memperhatikan etika penulisan ilmiah. Pelatihan ini ditujukan bagi guru SMP di wilayah Tangerang dan Jakarta, dilaksanakan secara daring, dan diikuti oleh 82 peserta. Hasil evaluasi menunjukkan adanya peningkatan signifikan kemampuan peserta. Nilai rata-rata pre-test tercatat sebesar 46,48 dan meningkat menjadi 69,27 pada post-test. Uji t berpasangan menghasilkan p-value 0,000 kurang dari  0,05 yang menunjukkan adanya perbedaan signifikan antara kemampuan peserta sebelum dan sesudah mengikuti pelatihan. Secara kualitatif, 21,95% peserta menyatakan “sangat setuju” dan 68,29% “setuju” bahwa pelatihan ini bermanfaat dalam meningkatkan wawasan dan keterampilan menulis karya ilmiah.Kata kunci: artificial intelligence, etik, publikasi, teknologi
MICE Implementation to Handle Missing Values in Rain Potential Prediction Using Support Vector Machine Algorithm Putri, Aina Latifa Riyana; Surarso, Bayu; SRRM, Titi Udjiani
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 4 (2023): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i4.16699

Abstract

Support Vector Machine (SVM) is a machine learning algorithm used for classification. SVM has several advantages such as the ability to handle high-dimensional data, effective in handling nonlinear data through kernel functions, and resistance to overfitting through soft margins. However, SVM has weaknesses, especially when handling missing values in data. The use of SVM must consider the missing values strategy chosen. Missing values in data mining is a serious problem for researchers because it causes many problems such as loss of efficiency, complications in data handling and analysis, and the occurrence of bias due to differences between missing data and complete data. To overcome the above problems, this research focuses on understanding the characteristics of missing values and handling them using the Multiple Imputation by Chained Equations (MICE) technique. In this study, we utilized secondary data experiments that contain missing values from the Meteorological, Climatological, and Geophysical Agency (called BMKG) related to predictions of potential rain, especially in DKI Jakarta. Identification of types or patterns of missing values, exploration of the relationship between missing values and other variables, incorporation of the MICE method to handle missing values, and the Support Vector Machine Algorithm for classification will be carried out to produce a more reliable and accurate prediction model for rain potential. It shows that the imputation method with the MICE gives better results than other techniques (such as Complete Case Analysis, Imputation Method Mean, Median, Mode, and K-Nearest neighbor), namely an accuracy of 89% testing data when applying the Support Vector Machine algorithm for classification.
CLUSTERING NEGARA BERDASARKAN SKOR PENGENDALIAN KONSUMSI TEMBAKAU MENGGUNAKAN ALGORITMA DBSCAN Joko Riyono; Pujiastuti, Christina Eni; Putri, Aina Latifa Riyana
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 8 No. 1 (2024): Volume 8, Nomor 1, Januari 2024
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v8i1.342

Abstract

Smoking is an activity that has a detrimental impact on the health of individuals, families, communities and the environment, both directly and indirectly. Therefore, it is necessary to control global tobacco consumption. The World Health Organization (WHO) in the Framework Convention on Tobacco Control (FTCT) developed the MPOWER strategy: a which countries can use to control tobacco consumption. In this study a clustering analysis of tobacco use control will be carried out based on the MPOWER score: a from WHO for each country using the Density Based Spatial Clustering of Applications with Noise (DBScan) algorithm. DBSCAN is a cluster formation algorithm based on the level of distance density between objects in a dataset. Using a density radius of 1.72 with a minimum point of 4 objects obtained from the kNNdisplot function on Rstudio produces 4 clusters, 75 data as noise, and the Davies Bouldin-Index value as the best cluster validity is 1.08118.
Co-Authors Amalia, Raisha Ayu, I Gusti De Bashiran, Himam Bayu Surarso Brian Nugraha Wiyono Christina Eni Pujiastuti Dewanti, Anggun Dody Prayitno Gabriella F Pandiangan Harnoko, Sri Namira Putri Joko Riyono Joko Riyono Kania, Putri Emas Mega Yuliani Ramme Nurkaromah Dwidayati, Nurkaromah Prameswari, Mayesq Pujiastuti, Christina Eni Puspa, Sofia Debi Putri, Dianing Novita Nurmala Setyaningsih, Destina Bekti Silaen, Ferdinan Supriyadi Supriyadi Supriyadi Syahid Nur Khomsyi Syahida, Shafa Tamara, Donna Nur Titi Udjiani SRRM Zahra, Indy Aurellia Az