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All Journal Statistika Jurnal Ilmiah Kursor Jurnal Sains Matematika dan Statistika MESIN PROSIDING SEMINAR NASIONAL CENDEKIAWAN JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika JTIK (Jurnal Teknik Informatika Kaputama) JISA (Jurnal Informatika dan Sains) Abdimasku : Jurnal Pengabdian Masyarakat Journal of Integrated and Advanced Engineering (JIAE) JUARA: JURNAL WAHANA ABDIMAS SEJAHTERA JURNAL ABDI MASYARAKAT INDONESIA (JAMIN) Jurnal Pengabdian Masyarakat dan aplikasi Teknologi METRIK SERIAL TEKNOLOGI DAN SAINS Metrik Serial Humaniora dan Sains Jurnal Pengabdian Sosial
Joko Riyono
Unknown Affiliation
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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PENGARUH LUAS PERMUKAAN ALUMINIUM FOAM SEBAGAI ANODA KORBAN PADA KOROSI KATODA Prayitno, Dody; Luthfan , M. Aufar; Riyono, Joko; Pujiastuti, Ch Eni
Metrik Serial Humaniora dan Sains Vol. 5 No. 2 (2024): Oktober 2024
Publisher : Konsorsium Cendekiawan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Tujuan penelitian adalah untuk mengetahui pengaruh luas permukaan aluminium foam sebagai anoda korban terhadap korosi katoda (baja ST37). Metode penelitian. Baja ST37 digunakan sebagai katoda. Aluminium foam dengan dihubungkan dengan katoda dengan menggunakan mur baut. Keduanaya lalu diletakkan pada bagian bawah pelampung. Pelampung lalu diletakkan pada mesin pembuat ombak. Media elektrolitnya adalah air laur dari Pantai Ancol, Jakarta utara, Indonesia. Metode kehilangan berat digunakan untuk mengukur laju korosi. Hasil penelitian. Aluminium foam dapat melindungan katoda (baja ST37) dari korosi. Peningkatan luas permukaan aluminium foam sebagai anoda korban dapat menurunkan laju korosi katoda (baja ST37).
EVALUATION OF PARTICLE SWARM ALGORITHM MODIFICATIONS ON SUPPORT VECTOR MACHINE HYPERPARAMETER OPTIMIZATION TUNING FOR RAIN PREDICTION Putri, Aina Latifa Riyana; Riyono, Joko; Pujiastuti, Christina Eni; Supriyadi
Jurnal Ilmiah Kursor Vol. 12 No. 4 (2024)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i4.411

Abstract

The Particle Swarm Optimization (PSO) algorithm, though simple and effective, faces challenges like premature convergence and local optima entrapment. Modifications in the PSO structure, particularly in acceleration coefficients (  and ), are proposed to address these issues. Techniques like Time Varying Acceleration Coefficients (TVAC), Sine Cosine Acceleration Coefficients (SCAC), and Nonlinear Dynamics Acceleration Coefficients (NDAC) have been implemented to enhance convergence speed and solution quality. This research evaluates various PSO modifications for improving convergence and robustness in rainfall potential prediction using Support Vector Machine (SVM) classification. The UAPSO-SVM algorithm C=0.82568 and γ=0.01960 excels in initial exploration, discovering more optimal global solutions with smaller variability. In contrast, TVACPSO-SVM shows gradual improvement but requires more iterations for stability, while SBPSO-SVM achieves the fastest convergence at iteration 14 but risks overfitting. Robustness analysis reveals all PSO-SVM variants maintain stable performance despite variations in dataset subset sizes, with accuracy stabilizing after a spike at 20%.. Therefore, PSO modifications enhance convergence speed and resilience to data fluctuations, improving their effectiveness for rainfall prediction.  
Customer Segmentation Analysis Using Random Forest & Naïve Bayes Method In The Case of Multi-Class Classification at PT. XYZ Puspa, Sofia Debi; Puspitasari, Fani; Riyono, Joko; Pujiastuti, Christina Eni; Bijlsma, David Leon; Leo, Joseph Andrew
Mathline : Jurnal Matematika dan Pendidikan Matematika Vol. 8 No. 4 (2023): Mathline: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/mathline.v8i4.532

Abstract

Cases of the COVID-19 pandemic are gradually decreasing every day in Indonesia, but the impact of the COVID-19 pandemic has greatly affected various sectors, especially the economy and business. Sales transactions have not yet reached the company's target due to weak public purchasing power. The accuracy of customer segmentation analysis and attractive promo voucher offers are needed to increase the opportunity for people's purchasing power for a product. This study aimed to predict the level of customer purchasing power using the random forest and naïve Bayes methods in the case of multi-class data classification at PT. XYZ. The classification is carried out to determine the type of promo voucher suitable to be offered to customers according to the level of customer purchasing power. The data used is historical daily transaction data from January 1, 2022, to December 31, 2022, which is the transition period for the COVID-19 pandemic. Evaluation using the random forest method produces an accuracy of 99.99%, while the naïve Bayes method produces an accuracy of 92.99%. The random forest and naïve Bayes methods can work very well on large data volumes. However, from the comparison results, it can be concluded that the performance of the random forest method is better than the naïve Bayes method in the multi-class classification case in predicting the level of customer purchasing power at PT. XYZ.
Decision Tree for Determining Hospital Treatment for Covid-19 Patients Based on Hematology Parameters Using the C5.0 Algorithm Riyono, Joko; Pujiastuti, Christina Eni; Supriyadi, Supriyadi; Prayitno, Dody; Putri, Aina Latifa Riyana
JISA(Jurnal Informatika dan Sains) Vol 7, No 2 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i2.2103

Abstract

The rapid spread of the COVID-19 disease, which occurred globally from late 2019 to the early 2020s, significantly impacted communities worldwide, requires early detection of COVID-19 which is very important for patients and also the people around them to be able to fight the COVID-19 pandemic. Therefore, a classification analysis will be carried out to make decisions regarding determining COVID-19 patients who do not require hospitalization or who require Regular Ward, Semi-Intensive Care Unit, or Intensive Care Unit (ICU) in hospitals based on hematology parameters from the Machine Learning Repository. Kaggle Dataset uses the C5.0 algorithm assisted by Rstudio software. It is also known that because the data contains missing data, it is also necessary to handle missing data using the Mean Method assisted by SPSS software. Performance evaluated using the Confusion Matrix method produces an accuracy value of 78% which is considered quite good, where testing with the C5.0 Algorithm uses a training and testing data ratio of 40:60. This research simplifies and speeds up medical decision-making, improving patient management. With COVID-19 declining, the method can be applied to enhance healthcare systems' accuracy and efficiency in handling other diseases or emergencies, ensuring better preparedness for future challenges.
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.
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
Enchancing Lung Disease Classification through K-Means Clustering, Chan-Vese Segmentation, and Canny Edge Detection on X-Ray Segmented Images Riyono, Joko; Pujiastuti, Christina Eni; Puspa, Sofia Debi; Supriyadi; Putri, Fayza Nayla Riyana
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1178

Abstract

The lungs are one of the vital organs in the human body. Not only play a role in the respiratory system, the lungs are also responsible for the human circulatory system. Supporting examinations can also facilitate medical workers in determining the diagnosis. Usually a lung examination is complemented by a chest X-ray examination procedure. This examination aims to see directly and assess the severity of lung conditions. With current technological advances, image analysis can be done easily. Through digital image processing methods, information can be obtained from images that can be used for analysis as a support for diagnoses in the world of health. Image segmentation is a method in which digital images are divided into several segments or subgroups based on the characteristics of the pixels in the image. In this study, clustering with the K-Means method will be carried out on the results of segmentation of x-ray images of lung diseases, namely Covid-19, Tuberculosis, and Pneumonia. The segmentation method that will be implemented is the Chan-Vese Method and the Canny Edge Detection Method. This research shows that the results of the accuracy of applying the K-Means Clustering method to Chan-Vese and Canny Edge-Based Image Segmentation are 80%.
The PENINGKATAN KETERAMPILAN GURU MELALUI PELATIHAN DASAR CODING & ANALISIS DATA STATISTIK UNTUK MENDUKUNG REVOLUSI INDUSTRI 4.0 Puspa, Sofia Debi; Riyono, Joko; Puspitasari, Fani; Pujiastuti, Christina Eni
Jurnal Abdi Masyarakat Indonesia (JAMIN) Vol 4 No 2 (2022): JURNAL ABDI MASYARAKAT INDONESIA (JAMIN)
Publisher : Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/jamin.v4i2.14132

Abstract

Seiring dengan perkembangan Revolusi Industri 4.0 di Indonesia, banyak sektor yang berdampak akibat Revolusi Industri 4.0 salah satunya yaitu pada bidang pendidikan. Dalam mewujudkan SDM unggul Indonesia yang mampu beradapatasi dengan perkembangan teknologi dibutuhkan kemampuan berpikir tingkat tinggi, berpikir kritis, kreatif dan inovatif. Selain itu, kemampuan computational thinking, dasar pemrograman komputer (coding) serta kemampuan analisis data statistik juga akan dibutuhkan. Tujuan dari kegiatan pelatihan dasar coding dan analisis data statistik adalah untuk meningkatkan pengetahuan dan meningkatkan keterampilan guru terkait dasar coding dan analisis data statistik dalam rangka mendukung Revolusi Industri 4.0. Kegiatan ini diikuti oleh 55 peserta dan pelatihan dilaksanakan secara daring. Berdasarkan hasil analisis kuantitatif dengan menggunakan data nilai pre-test dan post-test pada uji t berpasangan diperoleh p-value sebesar 0.00 lebih kecil dari taraf signifikansi (5%) sehingga H0 ditolak maka terdapat perbedaan yang signifikan antara rata-rata kemampuan sebelum dan sesudah pelatihan. Diketahui peningkatan rata-rata kemampuan peserta pelatihan sebesar 37.109 atau sekitar 88.7% dari rata-rata sebelum pelatihan.
PELATIHAN ANALISIS KORELASI DAN REGRESI DENGAN MENGGUNAKAN PERANGKAT LUNAK “R” UNTUK MENINGKATKAN KETERAMPILAN PENGOLAHAN DATA BAGI GURU Puspa, Sofia Debi; Joko Riyono; Fani Puspitasari; Christina Eni Pujiastuti
Jurnal Abdi Masyarakat Indonesia (JAMIN) Vol 6 No 1 (2024): JURNAL ABDI MASYARAKAT INDONESIA (JAMIN)
Publisher : Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/jamin.v6i1.17408

Abstract

Regression analysis is a statistical method that aims to determine the relationship between one or more independent variables and the dependent variable. Correlation expresses the degree of linear relationship between the two variables. The application of regression and correlation analysis is needed, especially for teachers, to determine what factors influence students' understanding of learning and it can be seen how strongly a variable influences other variables. This Community Service Activity is motivated by the need for educators to analyze the causes of factors that influence students's understanding of learning through data. This Community Service for partners aims to provide understanding related to Correlation and regression Analysis with" R" software. It is hoped that this Community service will make it easier for educators to decide on suitable learning models in class after knowing what factors influence student understanding in learning. Fifty-one participants attended this activity, and the training was carried out online. Based on the comparison of pre-test and post-test scores, there is an increased understanding of Correlation and Regression Analysis. The average increase in the trainees' ability was 34.14, 83.67% of the average before the training.
Co-Authors Bijlsma, David Leon Budiyanto , Chandra Ch. Eni Pujiastuti Christina Eni Pujiastuti Dody Prayitno Dody Prayitno Eni Pujiastuti Eni Pujiastuti, Christina Fani Puspitasari Fayza Nayla Riyana Putri Latifa Riyana Putri, Aina Leo, Joseph Andrew Luthfan , M. Aufar Prayitno , Dody Pujiastuti, Ch Eni Pujiastuti, Ch. Eni Pujiastuti, Christina Eni Pujiastuti, Eni Puspa, Sofia Debi Puspitasari, Fani Putri, Aina Latifa Riyana Putri, Dianing Novita Nurmala Putri, Fayza Nayla Riyana Rosyida Permatasari, Rosyida Supriyadi Supriyadi Supriyadi sutanti sutanti Syaifudin Syaifudin, Syaifudin Syaifudin, - -