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All Journal Jurnal Ilmiah Kursor Jurnal Sains Matematika dan Statistika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) JTIK (Jurnal Teknik Informatika Kaputama) JISA (Jurnal Informatika dan Sains) Abdimasku : Jurnal Pengabdian Masyarakat Madaniya Unnes Journal of Mathematics IJCOSIN : Indonesian Journal of Community Service and Innovation Jurnal Pengabdian Masyarakat dan aplikasi Teknologi Jurnal Pengabdian Sosial Parameter: Jurnal Matematika, Statistika dan Terapannya IJCOSIN : Indonesian Journal of Community Service and Innovation
Aina Latifa Riyana Putri
Telkom University
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Literasi Data Dengan Pembuatan Dashboard Dan Visualisasi Data Pada Data Runtun Waktu Dengan Looker Studio Dan RStudio riyono, joko; Pujiastuti, Christina Eni; Supriyadi, Supriyadi; Putri, Aina Latifa Riyana; Puspa, Sofia Debi
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 2 (2025): MEI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i2.2656

Abstract

Revolusi Industri 4.0 telah membawa pesatnya perkembangan teknologi informasi dan memberikan dampak besar pada berbagai bidang termasuk industri. Pilar teknologi terpenting dalam Revolusi Industri 4.0 meliputi big data, kecerdasan buatan, Internet of Things, komputasi awan, dan manufaktur aditif. PKM ini diadakan sebagai upaya dalam menambah kemampuan pengolahan dan visualisasi data khususnya di Runtun Waktu sehingga laporan menjadi menarik dan interaktif bagi mitra. Mitra PKM ini terdiri dari guru dan Gen Z dari wilayah Jabodetabek. PKM ingin agar Mitra PKM dapat memperoleh wawasan dan pengetahuan dari data yang kompleks serta memantau kondisi bisnis dan bidang lainnya yang dapat terupdate secara real time. Guna mengukur kemampuan Mitra sebelum dan sesudah mengikuti pelatihan, maka setiap Mitra PKM diminta menjawab Quiz sebelum dan sesudah pelatihan. Didasarkan hasil quiz dan kuesioner yang diberikan peserta PKM, sebanyak 85% setara dengan 110 dari total 130 peserta menilai bahwasanya pelaksanaan PKM berjalan dengan baik dan memberikan saran agar pelatihan dapat dilanjutkan dengan topik lain untuk menambah wawasan peserta di era digitalisasi saat ini. 
A Multi-Objective Particle Swarm Optimization Approach for Optimizing K-Means Clustering Centroids Latifa Riyana Putri, Aina; Riyono, Joko; Eni Pujiastuti, Christina; Supriyadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6533

Abstract

The K-Means algorithm is a popular unsupervised learning method used for data clustering. However, its performance heavily depends on centroid initialization and the distribution shape of the data, making it less effective for datasets with complex or non-linear cluster structures. This study evaluates the performance of the standard K-Means algorithm and proposes a Multiobjective Particle Swarm Optimization K-Means (MOPSO+K-Means) approach to improve clustering accuracy. The evaluation was conducted on five benchmark datasets: Atom, Chainlink, EngyTime, Target, and TwoDiamonds. Experimental results show that K-Means is effective only on datasets with clearly separated clusters, such as EngyTime and TwoDiamonds, achieving accuracies of 95.6% and 100%, respectively. In contrast, MOPSO+K-Means achieved a substantial accuracy improvement on the complex Target dataset, increasing from 0.26% to 59.2%. The TwoDiamonds dataset achieved the most desirable trade-off: it had the lowest SSW (1323.32), relatively high SSB (2863.34), and lowest standard deviation values, indicating compact clusters, good separation, and high consistency across runs. These findings highlight the potential of swarm-based optimization to achieve consistent and accurate clustering results on datasets with varying structural complexity.
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.
Literacy Review Study on the Implementation of Convolutional Neural Network Architecture in Segmentation and Classification of Lung Medical Images Riyono, Joko; Supriyadi, Supriyadi; Pujiastuti, Christina Eni; Puspa, Sofia Debi; Putri, Aina Latifa Riyana
JISA(Jurnal Informatika dan Sains) Vol 8, No 1 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i1.2172

Abstract

Medical image processing has become an essential aspect of healthcare, enabling accurate disease diagnosis and monitoring through advanced technologies. One of the most widely used methods in this domain is the Convolutional Neural Network (CNN), which has demonstrated high effectiveness in segmentation and classification tasks, particularly for chest X-ray images used in diagnosing lung-related diseases. This study aims to evaluate and analyze various CNN architectures implemented in lung X-ray imaging through a Systematic Literature Review (SLR) approach. The research explores the application, accuracy, challenges, and future opportunities of CNN-based models such as VGG, ResNet, AlexNet, and GoogLeNet. A total of 15 relevant studies published between 2019 and 2023 were selected after applying rigorous inclusion and exclusion criteria. The findings indicate that CNN architectures significantly enhance the accuracy of lung disease detection and support both segmentation and classification tasks. However, challenges such as dataset variability, model generalization, and ethical implications remain. This review provides comprehensive insights into CNN applications in medical imaging, emphasizing their potential and highlighting areas for further research.
Peningkatan Kualitas Produk dan Kapasitas Pemasaran Kelompok Wanita Tani Migunani Dusun Druwo Yogyakarta Siti Khomsah; Atika Ratnadewi; Aina Latifa Riyana Putri; Irwan Susanto; Rizal Wahyu Pratama; Mikhael Setia Budi; Khulika Malkan
Indonesian Journal of Community Service and Innovation Vol. 5 No. 3 (2025): Desember 2025
Publisher : LPPM IT Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/ijcosin.v5i3.10113

Abstract

Kelompok Wanita Tani (KWT) Migunani di Dusun Druwo, Kapanewon Sewon, Kabupaten Bantu, Yogyakarta bergerak di bidang pertanian dan usaha makanan seperti keripik dan kue basah. Meski cita rasa produk sudah baik, kualitas kemasan, mutu produk, dan pengelolaan keuangan masih kurang bagus. Produk dikemas dengan plastik sederhana tanpa deskripsi produk lengkap, serta pemasaran secara tradisional melalui bazar dan pasar desa. Lebih jauh, mereka tidak mencatat biaya dan pendapatan bisnisnya, sehingga mengakibatkan ketidakmampuan untuk menentukan laba rugi. Pengabdian masyarakat ini bertujuan memberikan solusi atas permasalahan tersebut dengan fokus pada tiga aspek: perbaikan kemasan, pemanfaatan digital marketing, dan pengelolaan keuangan usaha kecil. Metode pelaksanaan mencakup pelatihan dan pendampingan, yaitu: (1) pembuatan kemasan menarik termasuk desain logo dan label produk; (2) digital marketing melalui Instagram dan WhatsApp Business; (3) keuangan UMKM, seperti penggunaan QRIS, pencatatan arus kas, buku kas sederhana, perhitungan HPP dan BEP; serta (4) pendampingan sertifikasi halal. Hasil kegiatan menunjukkan peningkatan branding produk, dengan adanya logo, label halal, dan informasi produk lengkap. Anggota KWT juga mulai mampu mengelola keuangan secara lebih terstruktur dan memanfaatkan teknologi digital untuk pemasaran dan transaksi. Program ini diharapkan mampu meningkatkan daya saing produk KWT Migunani sekaligus memperkuat kemandirian ekonomi anggotanya.
Comparison of SARIMA Method, Holt-Winters Exponential Smoothing Method and Prophet Method in Inflation Data Forecasting Atika Ratna Dewi; Desty Mayang Pratiwi; Aina Latifa Riyana Putri
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp165-180

Abstract

This study discusses inflation forecasting in Indonesia using three time series methods, namely SARIMA, Holt-Winters Exponential Smoothing and Prophet, with monthly inflation data from January 2014 to October 2024. Inflation forecasting is important to maintain economic stability and support decision making in the monetary, fiscal, and investment sectors. The SARIMA method was chosen because of its ability to handle complex seasonal data, Holt-Winters Exponential Smoothing is used to accommodate seasonal patterns through alpha, beta, and gamma smoothing parameters, while Prophet was chosen because of its flexibility in handling nonlinear trends, seasonality, and special events such as holidays. The research steps include literature study, data collection, exploratory analysis, preprocessing, modeling with the three methods, and accuracy evaluation using Mean Absolute Percent Error (MAPE). The evaluation results show that the SARIMA(2,1,2)(1,0,1)^6model has a MAPE of 8.11%, better than Holt-Winters Exponential Smoothing of 11.75% and Prophet of 52.85%. Thus, SARIMA was chosen as the best model to forecast Indonesian inflation from November 2024 to April 2025. The prediction results were 6.19%, 5.40%, 4.96%, 4.94%, 4.57%, and 4.58%, respectively. This model is expected to be a reference in formulating strategic policies to maintain economic stability and improve public welfare.
Pelatihan Pemanfaatan Aplikasi Mendeley Dalam Sitasi dan Reference Manager bagi Guru Joko Riyono; Sofia Debi Puspa; Christina Eni Pujiastuti; Supriyadi Supriyadi; Aina Latifa Riyana Putri
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 2 (2026): MEI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i2.3039

Abstract

Di zaman digital sekarang, penguasaan pengelolaan sitasi dan referensi menjadi sangat penting sebagai sumber bahan acuan dalam penelitian dan penulisan akademik. Bagi para guru yang sering membuat makalah, laporan, atau bahan ajar, memiliki alat yang efisien untuk mengatur dan mengelola referensi adalah suatu keharusan. Mendeley, sebagai salah satu software manajemen referensi yang banyak digunakan, menawarkan solusi praktis untuk mengorganisir, menyimpan, dan menyitasi sumber informasi dengan mudah. Dengan pemahaman yang baik tentang Mendeley, diharapkan guru dapat meningkatkan efisiensi dalam penulisan akademis dan memberikan contoh positif kepada siswa dalam keterampilan literasi informasi. Oleh karena itu melalui Pelatihan Pemanfaatan Aplikasi Mendeley Dalam Sitasi dan Reference Manager bagi Guru, diharapkan akan dapat memberikan pengetahuan kepada para Guru. Pelatihan ini merupakan salah satu Progam Pengabdian Kepada Masyarakat yang diadakan oleh progam studi Teknik Mesin Fakultas Teknologi Industri Universitas Trisakti dengan mitra para Guru SMP 25 Tangerang dan beberapa guru sekolah menengah yang ada di Jabodetabek. Pelatihan "Pemanfaatan Aplikasi Mendeley dalam Sitasi dan Reference Manager bagi Guru" terbukti efektif dan berdampak positif dalam meningkatkan pemahaman serta keterampilan peserta dalam pengelolaan referensi ilmiah. Hal ini dibuktikan melalui peningkatan skor peserta dari pre-test ke post-test, dengan hasil uji hipotesis menunjukkan perbedaan yang signifikan secara statistik nilai t = 10,41 dan p-value = 2,61 × 10⁻²².
Convergence and Empirical Performance of Tanh-Based Adaptive Particle Swarm Optimization Joko Riyono; Aina Latifa Riyana Putri; Sofia Debi Puspa; Supriyadi Supriyadi; Christina Eni Pujiastuti; Fayza Nayla Riyana Putri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7247

Abstract

Particle Swarm Optimization (PSO) is a widely used population-based optimization method but faces challenges in premature convergence, leading to suboptimal solutions. To address this issue, this study proposes a Tanh-Based Acceleration Coefficient PSO (TB-PSO), where the acceleration coefficients are modified using the hyperbolic tangent (tanh) function. The smooth and continuous behavior of tanh enables gradual coefficient updates, limits excessive particle velocities, and maintains swarm diversity, thereby improving convergence stability and balancing exploration and exploitation. The convergence theorem analysis confirms that TB-PSO meets stability criteria before being evaluated on unimodal and multimodal benchmark functions in 10 and 30 dimensions. Its performance is compared against several PSO variants, including TVAC-PSO, SCAC-PSO, NDAC-PSO, and SAC-PSO. In the 10-dimensional experiments, TB-PSO achieves the best overall final ranking based on the average and standard deviation of best solution, ranking first for functions f₃ and f₅, second for f₂ with only a marginal difference from the best-performing method, and remaining competitive for f₁ and f₄. These results indicate superior solution quality and stable convergence. For the 30-dimensional benchmark functions, TB-PSO ranks first for f₂, second for f₅, and third for f₁, f₃, and f₄ based on the same evaluation criteria. Although its ranking decreases compared to the 10-dimensional case, TB-PSO remains competitive, reflecting the increased complexity of high-dimensional optimization problems. Overall, the results demonstrate that the tanh-based acceleration coefficient modification effectively enhances PSO performance, particularly in lower-dimensional search spaces, while maintaining robustness in higher-dimensional scenarios.
A COMPARATIVE EVALUATION OF SARIMA AND FUZZY TIME SERIES CHEN MODELS FOR RAINFALL FORECASTING IN MAKASSAR Gavrilla Claudia; Atika Ratna Dewi; Aina Latifa Riyana Putri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1389-1404

Abstract

High rainfall intensity in Makassar often leads to flooding. Therefore, forecasting the amount of rainfall is necessary as a reference for taking appropriate mitigation measures. This study was conducted to select the best model between the SARIMA and Fuzzy Time Series (FTS) Chen based on a comparison of their forecasting accuracy, as well as to forecast the amount of rainfall in Makassar for 2024 using the best model. For this study, monthly rainfall data covering the period from January 2014 to December 2024 were collected from the official website of the Central Statistics Agency (BPS) Makassar. Based on the analysis results, SARIMA(7,2,3)(1,1,1)12 was selected as the best model, with an MAE value of 2.654 and an RMSE value of 3.846. The contribution of this study lies in providing an empirical comparison between SARIMA and FTS Chen for rainfall forecasting in tropical regions. However, the limitation of this study is that the forecasting relies solely on historical rainfall data, without incorporating other meteorological variables that may influence rainfall patterns.
Co-Authors Adhystira Raihannoeza Almadiva Amalia, Raisha Atika Ratna Dewi Atika Ratnadewi Ayu, I Gusti De Bashiran, Himam Bayu Surarso Brian Nugraha Wiyono Christina Eni Pujiastuti Christina Eni Pujiastuti Christina Eni Pujiastuti Desty Mayang Pratiwi Dewanti, Anggun Dianing Novita Nurmala Putri Dody Prayitno Eni Pujiastuti, Christina Fayza Nayla Riyana Putri Gabriella F Pandiangan Gavrilla Claudia Harnoko, Sri Namira Putri Irwan Susanto Joko Riyono Joko Riyono Joko Riyono Kania, Putri Emas Kartika Dian Kurniasari Khoirun Niamah Khulika Malkan Mega Yuliani Ramme Mikhael Setia Budi Nurkaromah Dwidayati, Nurkaromah Prameswari, Mayesq Pujiastuti, Christina Eni Puspa, Sofia Debi Rizal Wahyu Pratama Setyaningsih, Destina Bekti Silaen, Ferdinan Siti Khomsah, Siti Sofia Debi Puspa Sofia Debi Puspa Supriyadi Supriyadi Supriyadi Supriyadi Supriyadi Supriyadi Supriyadi Syahid Nur Khomsyi Syahida, Shafa Tamara, Donna Nur Titi Udjiani SRRM Zahra, Indy Aurellia Az