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All Journal Jurnal Simetris Telematika Jurnal Transformatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Pengembangan Rekayasa dan Teknologi Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) Journal of Computer and Information Systems Ampera Jurnal Pengabdian Masyarakat Indonesia JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Mitra Masyarakat (JPMM) Tematik Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal DIMASTIK
Rastri Prathivi
Universitas Semarang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Pelatihan Moodle sebagai persiapan pembelajaran Blended Learning di SMP IT Bina Amal Gunung Pati Semarang : Persiapan Pembelajaran Blended Learning Untuk Guru-Guru Sekolah Menengah Pertama (SMP) IT Bina Amal Gunung Pati Semarang Sri Handayani; Edi Widodo; Rastri Prathivi
Jurnal Pengabdian Masyarakat Indonesia Vol 2 No 4 (2022): JPMI - Agustus 2022
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpmi.673

Abstract

Berdasarkan Surat Keputusan Bersama (SKB) 4 menteri tersebut, maka sekolah mulai melaksanakan pembelajaran tatap muka terbatas dengan jumlah siswa hadir maksimal 50% dari total kapasitas kelas yang ditentukan, yaitu maksimal 20 orang dalam setiap kelas. Dengan kenyataan tersebut, maka sekolah harus menyiapkan sumber daya manusia untuk mampu melaksanakan pembelajaran bersama antara siswa yang hadir di sekolah dan yang mengikuti pelajaran dari rumah, agar tujuan dari pembelajaran tetap tercapai dengan konsep pembelajaran blended learning. Mitra yang akan bekerja sama dalam pengabdian kepada masyarakat ini adalah Sekolah Menegah Pertama Islam Terpadu Bina Amal (SMP-IT Bina Amal) Gunung Pati Semarang. Adanya rencana blended learning tersebut, tentu perlu dipersiapkan para guru, salah satunya adalah dengan mengikuti pelatihan persiapan pembelajaran blended learning. Metode yang digunakan dalam pelatihan ini adalah dengan menggunakan metode praktikum yaitu cara penyajian pembelajaran kepada peserta dengan melakukan penerapan langsung menggunakan komputer sehingga peserta akan mengalami dan membuktikan bagaimana menggunakan platform Moodle. Diharapkan dengan mengikuti kegiatan pelatihan persiapan pembelajaran blended learning ini, para guru mendapat gambaran dan kebijakan tentang apa saja yang harus diperhatikan dan disiapkan bila blended learning dilaksakan. Indikator keberhasilan dari kegiatan pelatihan ini diperoleh dengan cara mengolah hasil kuesioner yang diisi peserta sebelum mengikuti pelatihan (pre test) dan setelah mengikuti pelatihan (post test) dan diperoleh adanya peningkatan pemahaman dan ketrampilan peserta dalam menggunakan Moodle yaitu sebesar 91.7%
Perbandingan Algoritma Support Vector Machine dan Decision Tree untuk Klasifikasi Performa Perusahaan Utomo, Mario; Prathivi, Rastri
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5278

Abstract

The number of stock exchange investors in Indonesia reached 5.34 million by the end of December 2023. This figure is dominated by millennial generation investors, indicating a growing confidence in the fundamentals and economic prospects of the Indonesian capital market. However, the lack of financial literacy among this generation often results in ineffective and high-risk investments. Many millennials choose stocks based on short-term trends or recommendations that lack analysis. To address this issue, a more structured approach to stock selection is required. One method that can be employed is the classification of a company's performance based on its performance using various financial indicators and ratios. As the performance of a company affects the movement of its stock value, this research will compare Support Vector Machine and Decision Tree with the One Against All approach in classifying company performance. The features used for the classification of company performance consist of three financial ratios: profitability (ROA), liquidity (CR), and leverage (DER). The labels or targets in the classification are divided into three categories: normal, good, and unfavorable. This research will consider evaluations such as accuracy, cross validation, and confusion matrix. The results of the Support Vector Machine (SVM) algorithm demonstrated an accuracy of 86.67%, while the Decision Tree (DT) algorithm exhibited an accuracy of 93.33%. Consequently, the DT algorithm produced more accurate results than the SVM algorithm in classification. The number of stock exchange investors in Indonesia reached 5.34 million by the end of December 2023. This figure is dominated by millennial generation investors, indicating a growing confidence in the fundamentals and economic prospects of the Indonesian capital market. However, the lack of financial literacy among this generation often results in ineffective and high-risk investments. Many millennials choose stocks based on short-term trends or recommendations that lack analysis. To address this issue, a more structured approach to stock selection is required. One method that can be employed is the classification of a company's performance based on its performance using various financial indicators and ratios. As the performance of a company affects the movement of its stock value, this research will compare Support Vector Machine and Decision Tree with the One Against All approach in classifying company performance. The features used for the classification of company performance consist of three financial ratios: profitability (ROA), liquidity (CR), and leverage (DER). The labels or targets in the classification are divided into three categories: normal, good, and unfavorable. This research will consider evaluations such as accuracy, cross validation, and confusion matrix. The results of the Support Vector Machine (SVM) algorithm demonstrated an accuracy of 86.67%, while the Decision Tree (DT) algorithm exhibited an accuracy of 93.33%. Consequently, the DT algorithm produced more accurate results than the SVM algorithm in classification.
Implementasi K-Means Untuk Pengelompokan Makanan Cepat Saji Bagi Penderita Penyakit Obesitas Pradvenanta, Yoannes Dion; Prathivi, Rastri
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5279

Abstract

One in eight people in the world lives with obesity, a statistic that is worrying as it shows a significant increase compared to 1990. Obesity in adults has more than doubled, and obesity among adolescents and children has quadrupled. One factor in obesity is poor food quality. A lot of people who don't pay much attention to the quality of their food one of them is eating fast food because fast food consumption can be said to be good if the meal frequency is 1 time a week, if more than that and excess is said not good. Thus, there is a need for a fast food grouping model that helps obese people choose fast foods. The K-means algorithm is one of the ideal models for grouping fast foods. The results of the analysis using the elbow method show k=5, then consider three evaluations against the k=5 value: Sum Square Error (SSE), Silhouette Score, and Davies Bouldin Index (DBI). The results were data segmented taking into account the negative and positive nutrient content for obese patients. The data segmentation results found a fairly healthy cluster on label_0 with 244 data and an unhealthy cluster in label_2 with 25 data. From the cluster label_0, 244 of the data could be a healthy fast food choice for obesity patients
PENINGKATAN PEMAHAMAN E-LEARNING MENGGUNAKAN APLIKASI EDMODO BAGI SISWA MA AL WATHONIYYAH SEMARANG Nugroho, Atmoko; Prathivi, Rastri
TEMATIK Vol. 1 No. 1 (2021): Januari
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/tmt.v1i1.1870

Abstract

MA Al Wathoniyyah Semarang adalah Madrasah Aliyah dengan kelas X, XI, XII yang tentu mendapatkan pelajaran Teknologi Informasi dan Komunikasi (TIK) di sekolahnya. Dari hasil wawancara yang dilakukan oleh tim Pengabdian Kepada Masyarakat (PKM) kepada pihak sekolah MA Al Wathoniyyah Semarang didapatkan fakta bahwa di dalam lingkungan MA Al Wathoniyyah Semarang   untuk penggunaan Edmodo belum maksimal digunakan dalam pembelajaran di sekolah. Khalayak sasaran ditujukan bagi Siswa MA Al Wathoniyyah Semarang sebanyak 20 hingga 25 siswa.Metode yang digunakan dalam PKM ini dalam bentuk seminar atau ceramah. Untuk tempat pengabdian masyarakat ini berada pada lingkungan MA Al Wathoniyyah Semarang atau pada Laboratorium Komputer FTIK USM yang nantinya akan berlangsung selama 3 jam dan mengenai waktu pelaksanaan pengabdian masyarakat ini estimasi pada bulan Desember 2019.Hasil yang dicapai dari kegiatan PKM ini adalah peningkatan kemampuan siswa MA Al Wathoniyyah Semarang untuk memahami konsep E-Learning dan implementasinya menggunakan Edmodo.
Deteksi dan Penghitung Keramaian Menggunakan You Only Look Once 3 Tiny dan Raspberry Pi Hirzan, Alauddin Maulana; Prathivi, Rastri; Hanif, Mohammad Burhan
Journal of Computer and Information Systems Ampera Vol. 4 No. 3 (2023): Journal of Computer and Information Systems Ampera
Publisher : APTIKOM SUMSEL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalcisa.v4i3.417

Abstract

Keramaian adalah aspek sosial yang tidak bisa dipisahkan dari masyarakat. Baik untuk keperluan bersosialisasi hingga menyampaikan suara melalui demonstrasi, masyarakat akan membentuk keramaian untuk mencapai tujuan tersebut. Keramaian ini tentu saja memiliki dampak positif, namun tetap memiliki dampak negatif berupa kemungkinan terjadinya provokasi dan membentuk anarkisme. Oleh karena itu banyak penelitian yang memiliki fokus untuk melakukan deteksi keramaian. Namun sayangnya, penelitian yang telah dilakukan ini memiliki kelemahan di mana model yang dibuat tidak mampu melakukan deteksi jarak antar satu manusia dengan manusia lainnya. Untuk mengatasi permasalahan tersebut, penelitian ini mendesain sebuah model deteksi menggunakan YOLOv3-Tiny yang diimplementasikan ke dalam perangkat Internet of Things. Dari proses pengujian menggunakan 60 gambar dengan resolusi yang berbeda. Pengujian ini berhasil mendeteksi keramaian dan jarak antar manusianya. Model ini membutuhkan CPU sebanyak 76,22%. Untuk memori membutuhkan 454,78MB untuk proses, 405,61MB untuk data, dan 130,94MB untuk memori virtual. Dari hasil ini bisa disimpulkan bahwa model yang diusulkan mampu mendeteksi keramaian dengan baik tanpa mengalami kesalahan karena kurangnya kemampuan komputasi.
Evaluating the Popularity of Programming Languages in Indonesia using the MABAC Method Widodo, Edi; Prathivi, Rastri; Hadi, Soiful
Jurnal Transformatika Vol 21, No 1 (2023): July 2023
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.7001

Abstract

In today's fast-paced digital era, the selection of a programming language plays a crucial role in the success of software development projects. This research aims to create an index of popularity for programming languages using the multi-attributive border approximation area comparison (MABAC) method. The study considers four data sources, including Jobstreet.Com, LinkedIn.Com, Google Trends, and Tiobe.com, to obtain the necessary information for evaluating the popularity of programming languages in Indonesia. The data range for this study is from May 1, 2020, until April 31, 2021. The results of the study indicate that the top ten programming languages in terms of popularity in Indonesia are Java, SQL, php, JavaScript, C, C++, python, C#, Visual Basic, and Assembly. The index can serve as a useful guide for strategic decision-making regarding the selection of programming languages for addressing the needs of the information technology market in Indonesia. The study's findings can be useful for software developers, IT professionals, and decision-makers in organizations who need to select a programming language for their software projects in Indonesia. The MABAC method used in this study can also be applied to other contexts for evaluating the popularity of programming languages.
Komparasi Metode BERT, VADER, dan RoBERTa untuk Analisis Sentimen Masyarakat terhadap Keputusan Pasangan Nurdewanti, Debi Safa; Prathivi, Rastri
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6306

Abstract

This research discusses the phenomenon of childfree in Indonesia, which is increasingly being discussed in line with social and economic changes. Although a negative stigma is still attached to a couple's decision not to have children, public awareness of the childfree option continues to increase. This study aims to analyze public sentiment towards childfree decisions using three sentiment analysis methods, namely BERT, RoBERTa, and VADER. The analysis results show that the BERT method has the highest accuracy of 99%, signaling its ability to classify sentiment very accurately. In contrast, the RoBERTa and VADER methods show lower accuracy, at 50% and 41% respectively. Both methods had difficulty in distinguishing the sentiment classes, which resulted in many misclassifications. Evaluation using the confusion matrix shows that RoBERTa and VADER have a significant number of misclassifications, with RoBERTa having 9 FPs and 19 FNs, and VADER having 16 FPs and 84 FNs. Meanwhile, BERT has almost no errors in classification, with a total FP of 0 and FN of 1. These results confirm that the BERT method is superior for sentiment analysis of the childfree phenomenon compared to the RoBERTa and VADER methods. This research provides insight into how people view the childfree phenomenon and finds the best sentiment analysis method among the three methods.
COMPARISON OF MOBILENET AND CNN METHODS FOR IDENTIFYING TOMATO LEAF DISEASES Andrianto, Diky; Prathivi, Rastri; Liu, Meifang
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.3477

Abstract

Tomato plants are usually easily attacked by diseases, either viruses or fungi, resulting in a significant reduction in the quality and quantity of crop production. Tomato production is at risk from various diseases affecting the leaves. Early diagnosis of these diseases allows farmers to take preventive action and protect their crops. The use of artificial intelligence, especially deep learning, has greatly improved plant disease detection systems. Advances in computer vision, particularly Convolutional Neural Networks (CNN), have shown reliable results in image classification and identification. Below is previous research on identifying tomato leaf diseases.
Klasifikasi Gempa Bumi Berdasarkan Magnitudo Menggunakan Metode Logistic Regression Mar’atuzzulfa, Salma; Prathivi, Rastri; Susanto, S
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.564

Abstract

The purpose of this study is to categorize areas in Indonesia that are potentially prone to earthquakes using the logistic regression algorithm. Variables such as latitude, longitude, depth, and magnitude are used to analyze 118 data points of natural disasters that occurred in Indonesia in 2023. As much as 40% of the data is used for testing, while 60% is used for training. The magnitudes are high, medium, and low. The logistic regression method is used to determine the level of health in the area and assess the relationship between variables. The study's findings indicate that the model has an accuracy of 93.62%, precision of 94%, recall of 93%, and F1 skor of 93% overall. In addition, the evaluation of the model's kinerja using the confusion matrix indicates that algorithms might associate a given category with a high sensitivity to error. By identifying data points and creating Logistic regression can assist in developing more effective bencana mitigation strategies by identifying data points and producing accurate predictions. As a result, it is believed that the general public can reduce the amount of dampak gempa bumi.
Analisis Preferensi Penonton Anime berbasiskan Genre Film menggunakan Metode K-Means Zalzabila, Niken; Prathivi, Rastri
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.565

Abstract

This study aims to analyze anime audience preferences based on genres using the K-Means clustering algorithm. The dataset consists of 100 popular anime titles with features such as ratings, votes, and genres. The research steps include data preprocessing, clustering with the Elbow method to determine the optimal number of clusters, and applying the K-Means algorithm. The clustering results revealed four clusters with unique characteristics, highlighting differences in popularity and genre preferences. Evaluation using the Confusion Matrix shows a model accuracy of 95%, while the Silhouette score of 0.285 indicates adequate cluster separation. These findings are expected to provide insights for streaming platforms to deliver more personalized and relevant anime recommendations to viewers.
Co-Authors Alauddin Maulana Hirzan Andrianto, Diky April Firman Daru Astrid Novita Putri, Astrid Atmoko Nugroho Cholil, Saifur Rohman Ciptandini, Madyantari Ipmas Dewanti, Tita Risa Edi Widodo Edi Widodo, Edi Elfitrianna, Ikka Ayu Febrian Wahyu Christanto Galet Guntoro Setiaji Hadi, Soiful Hanif, Mohammad Burhan Hetty Catur Ellyawati, Hetty Catur karunia, windy Lenny Margaretta Huizen Liu, Meifang Mar’atuzzulfa, Salma Nurdewanti, Debi Safa Pradvenanta, Yoannes Dion Qurinta Shinta Rahmaeda, Shafara Rastri Prathivi Rosidah, Nafiati Saifur Rohman Cholil Sara Sharifzadeh Siti Asmiatun, Siti Soiful Hadi Sri Handayani Susanto Susanto Titin Winarti Titis Handayani Tria Ardianita Tunggal, Anisa Utomo, Mario Victor Gayuh Utomo Windy Yunita Kurniawati, Yunita Zalzabila, Niken