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All Journal Jurnal Rekayasa Sistem Industri KALAMATIKA Jurnal Pendidikan Matematika PRISMA SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Conference on Innovation and Application of Science and Technology (CIASTECH) JKPM (Jurnal Kajian Pendidikan Matematika) M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jusikom: Jurnal Sistem Informasi Ilmu Komputer Buana Ilmu Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknik Informatika (JUTIF) KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Inovasi Penelitian dan Pengabdian Masyarakat DECODE: Jurnal Pendidikan Teknologi Informasi Scientific Student Journal for Information, Technology and Science Jurnal Algoritma Jurnal Komtika (Komputasi dan Informatika)
Lestari, Santi Arum Puspita
Universitas Buana Perjuangan Karawang
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Pemantauan Tingkat Derajat Keasaman Air Akuarium Dengan Metode Fuzzy Logic Tsukamoto Iyan Khoerniyah; Deden Wahiddin; Santi Arum Puspita Lestari
Scientific Student Journal for Information, Technology and Science Vol. 2 No. 2 (2021): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (318.607 KB)

Abstract

Akuaium merupakan tempat kehidupan ikan hias. Ikan hias dapat hidup dan tumbuh dengan baik, diperlukan beberapa perawatan yang baik diantarannya yaitu pemberian pakan, oksigen, cahaya, dan kondisi air yang baik. Kondisi pH pada air sangat mempengaruhi terhadap kesehatan ikan hias. Kurang memperhatikan kondisi air akuarium dapat mengakibatkan ikan hias mati. Sehingga pemilik harus melakukan pengecekan secara berkala. Salah satu salusi yang dapat digunakan untuk menangani permasalahan tersebut yaitu dengan merangkai alat monitoring pH air secara otomatis menggunakan arduino uno. Hasil dari rangkain yang dibuat dapat mengontrol pH dengan menambahkan cairan pH up dan down direalisaikan dengan katup solenoid. Sistem pengontrolan pH dapat dilakukan jika nilai pH dinyatakan pH tinggi atau rendah. Jika sensor membaca pH tinggi maka cairan down akan masuk kedalam air dan bila pH rendah maka cairan up akan masuk kedalam air dan akan menstabilkan nilai pH tetap normal. Hasil yang didapat dari pengujian sensor pH SEN161 Sebanyak 6 kali diperoleh nilai selisih keseluruhan 3.3 dengan nilai rata-rata 0.55 dan persentase erorr 0.391 dengan nilai rata-rata erorr 0.065.
DETEKSI SERANGAN PERETAS MENGGUNAKAN HONEYPOT COWRIE DAN INTRUSION DETECTION SYSTEM SNORT Roby Rupiat; Sutan Faisal; Tohirin Al Mudzakir; Santi Arum Puspita Lestari
Conference on Innovation and Application of Science and Technology (CIASTECH) CIASTECH 2020 "Peranan Strategis Teknologi Dalam Kehidupan di Era New Normal"
Publisher : Universitas Widyagama Malang

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Abstract

Sistem keamanan server menjadi sangat penting dalam menjaga sebuah data. Dinas komunikasi informatika persandian dan statistik kabupaten Bekasi saat ini hanya menggunakan firewall sebagai sistem keamanan server, sehingga dapat menyebabkan aktifitas serangan yang dapat mengakibatkan kerugian kehilangan data. Permasalahan tersebut, perlu dibangun sistem keamanan server yang bisa mengamankan data pada sistem server. Penelitian ini menggunakan sistem keamanan server honeypot cowrie dan IDS snort. Metode pengembangan sistem yang digunakan yaitu Network Development Life Cycle (NDLC). Proses pengujian yang digunakan yaitu teknik serangan portscanning, teknik serangan bruteforce attack, dan melakukan blok ip address peretas. Pengujian dengan teknik portscanning dapat menghasilkan informasi penting pada suatu jaringan dan mendeteksi port yang terbuka, di antaranya port 22 yaitu ssh (secure shell). Teknik serangan bruteforce attack dapat menghasilkan kombinasi username dan password yang ada pada sistem server secara ilegal. Honeypot cowrie dapat menjebak peretas dengan server palsu yang telah dibuat. IDS snort dapat mendeteksi serangan yang masuk pada sistem server, kemudian IDS snort dapat memblokir ip address peretas yang melakukan serangan terhadap sistem server. Sehingga data yang ada pada sistem server dapat terjaga dengan aman, karena setiap aktiftas peretasan dapat dipantau oleh administrator untuk ditindak lebih lanjut.
ANALISIS KESULITAN BELAJAR MATEMATIKA DISKRIT MAHASISWA TEKNIK INFORMATIKA Kusumaningrum, Dwi Sulistya; Puspita Lestari, Santi Arum
PRISMA Vol 8, No 2 (2019): Jurnal PRISMA Volume 8, No 2 tahun 2019
Publisher : Universitas Suryakancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/jp.v8i2.717

Abstract

ABSTRACTAsosiasi Perguruan Tinggi Informatika dan Komputer (APTIKOM) states that learning outcomes of Informatics on mathematical topics are discrete mathematics. This study aims to examine the results of students learning discrete mathematics, and examine what factors are causing students difficulty learning discrete mathematics. The method used is a mix method which is qualitative methods and quantitative methods. Data collection as the result of discrete mathematics learning outcomes, and mathematical learning difficulty questionnaire data. The population used is students of Informatics at Buana Perjuangan University Karawang with a sample of 65 students taking discrete mathematics courses in the 2018/2019 school year. The results showed that most students still had difficulty learning discrete mathematics. This is because an average value is 66,4 from the value of a test value is 55. This Test Value is the minimum passing grade. While the factors that cause discrete mathematics learning difficulties are divided into 2 classifications namely 6 influential indicators and 2 quite influential indicators. Keyword: Learning Difficulties, Learning Outcomes, Discrete Mathematics
Comparison of K-Nearest Neighbors and Convolutional Neural Network Algorithms in Potato Leaf Disease Classification Nurmayanti, Trisya; Hartini, Dina; Rohana, Tatang; Lestari, Santi Arum Puspita; Wahiddin, Deden
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5337

Abstract

tatang.rohana@ubpkarawang.ac.id3, santi.arum@ubpkarawang.ac.id4, deden.wahiddin@ubpkarawang.ac.id5ABSTRACTPotato production in Central Java was recorded to have decreased by 10.77% by the Central Statistics Agency (BPS), from 278,717 tons in 2022 to 248,700 tons in 2023. This decline is due to the fact that potatoes are susceptible to diseases such as late blight and dry spot (early blight) which can significantly reduce yields. This study aims to evaluate the performance of Convolutional Neural Network (CNN) with VGG16 architecture and K-Nearest Neighbors (KNN) to find the best method for potato late blight classification. The dataset used consists of 1500 potato leaf images divided into training, validation, and testing. This research uses pre- processing including resizing, rescaling, and data augmentation. The results show that CNN with the VGG16 model is superior in classifying potato leaf diseases compared to KNN with the MobileNetV2 model. CNN produced an accuracy of 96% while KNN with the MobileNetV2 model obtained an accuracy of 93%. These results can be used as a powerful tool in supporting potato leaf disease identification. This model makes a significant contribution to the development of disease identification techniques through digital image processing.Keywords: Potato Leaf Disease, Convolutional Neural Network, VGG16, K-Nearest
Mengintegrasikan Prinsip Pembangunan Berkelanjutan dalam Pembelajaran Matematika untuk Merangsang Keterampilan Berkelanjutan pada Generasi Mendatang Lestari, Santi Arum Puspita; Nurapriani, Fitria; Kusumaningrum, Dwi Sulistya
Jurnal Rekayasa Sistem Industri Vol. 13 No. 1 (2024): Jurnal Rekayasa Sistem Industri
Publisher : Universitas Katolik Parahyangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26593/jrsi.v13i1.7167.1-10

Abstract

This research constitutes a literature review employing a qualitative approach, analyzing scholarly articles, books, and other documents related to sustainable development. This article aims to summarize and analyze previous studies concerning sustainable development in the context of mathematics education, as well as strategies that can be employed to integrate the principles of sustainable education. Integrating the principles of sustainable development into education, including mathematics education, is crucial in fostering a more environmentally responsible society and promoting sustainability across all sectors. However, its implementation remains limited. Educators face various challenges, including a lack of time, resources, and understanding of sustainable education, along with a dearth of supportive teaching materials. The principles of sustainable development can serve as a framework for developing curricula and teaching practices that are more sustainable. Educators can select mathematical problems related to environmental or social issues, discuss relevant mathematical concepts in connection with these problems, and help students comprehend the impact of mathematical decisions on the environment and society. Integrating the principles of sustainable development into mathematics education not only aids in producing a generation with sustainable skills but also motivates students to learn mathematics in more engaging and meaningful ways. A learning approach centered around sustainable development can be an effective way to prepare students for a sustainable future. The article also underscores the necessity for curriculum development, training, and professional advancement for educators.
OPTIMAL STUDY OF REAL-ESTATE PRICE PREDICTION MODELS USING MACHINE LEARNING Maulana, Ikhsan; Siregar, Amril Mutoi; Lestari, Santi Arum Puspita; Faisal, Sutan
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2565

Abstract

Everyone wants a place to live, especially close to work, shopping centers, easy transportation, low crime rates and others. Pricing must also pay attention to external factors, not just the house. Determining this price is sometimes difficult for some people. Therefore, the aim of this research is to predict real-estate prices by taking these factors into account. Prediction results are very useful for sellers who have difficulty determining prices and also for prospective buyers who are confused when making financial plans to buy a house in the desired neighborhood. The dataset used in this research was obtained from Kaggle and consists of 506 samples with 14 attributes. Several machine learning algorithms, such as Extra Trees (ET), Support Vector Regression (SVR), Random Forest (RF), eXtreme Gradient Boosting (XGB), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), and CatBoost, used to predict real-estate prices. This research uses Principal Component Analysis (PCA) for feature selection techniques in data sets after the preprocessing phase and before model building. The highest accuracy model obtained is CatBoost with GridSearchCV, this model has been cross validated so there is very little chance of overfitting when given new data. The SVR model with a poly kernel uses a Principal Component (PC) of 10 and GridSearchCV gets an R2 Score of 0.87, a very large number close to the score of CatBoost with GridSearchCV.
IMPLEMENTATION OF DIABETES PREDICTION MODEL USING RANDOM FOREST ALGORITHM, K-NEAREST NEIGHBOR, AND LOGISTIC REGRESSION Pratama, Rio; Siregar, Amril Mutoi; Lestari, Santi Arum Puspita; Faisal, Sutan
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2593

Abstract

Diabetes is a serious metabolic disease that can cause various health complications. With more than 537 million people worldwide living with diabetes in 2021, early detection is crucial to preventing further complications. This research aims to predict the risk of diabetes using machine learning algorithms, namely Random Forest (RF), K-Nearest Neighbor (KNN), and Logistic Regression (LR), with the diabetes dataset from UCI. Previous research has explored a variety of algorithms and techniques, with results varying in accuracy. This research uses a dataset from Kaggle which consists of 768 data with 8 parameters, which are processed through pre-processing and data normalization techniques. The model was evaluated using metrics such as accuracy, confusion matrix, and ROC-AUC. The results showed that Logistic Regression had the best performance with 77% accuracy and AUC 0.83, compared to KNN (75% accuracy, AUC 0.81) and Random Forest ( 74% accuracy, AUC 0.81). These findings emphasize the importance of appropriate algorithm selection and good data pre-processing in diabetes risk prediction. This study concludes that Logistic Regression is the most effective method for predicting diabetes risk in the dataset used.
SENTIMENT ANALYSIS OF THE SAMBARA APPLICATION USING THE SUPPORT VECTOR MACHINE ALGORITHM Firdaus, Thoriq Janati; Indra, Jamaludin; Lestari, Santi Arum Puspita; Hikmayanti, Hanny
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2673

Abstract

Rapid technological developments have opened up new opportunities for public services by utilizing digital application innovations. One example is the West Java Samsat Mobile (SAMBARA) designed by the West Java Provincial Revenue Agency (BAPENDA). The SAMBARA application is expected to accelerate annual vehicle tax payment obligations, but several reviews on the Playstore show user dissatisfaction with SAMBARA's performance. This study aims to conduct a sentiment analysis of SAMBARA application reviews using the Support Vector Machine algorithm. SAMBARA user review data on Google Playstore was collected using the python programming language google play scraper library on google colabolatory resulting in 1620 data on January 2, 2024. The data pre-processing stage involves various steps such as data cleaning, lowercase conversion, tokenization, stemming, stop words removal, normalization, and the use of the TF-IDF method. The data is then labeled positive and negative, positive for reviews with scores of 4 and 5 and negative labels for reviews with scores of 1 to 3. The Support Vector Machine (SVM) algorithm is used for classification, a well-known method for accurate classification. Model evaluation was conducted using a confusion matrix to calculate the precision, recall, and F1-Score values. The evaluation results provide an overview of the performance of the classification algorithm in grouping user reviews into positive and negative categories. The evaluation results show that the SVM algorithm provides quite good performance with an accuracy value of 88.75%, precision 87.51%, recall 81.25%, and F1-Score 83.71% which can be the basis for improving the quality of service of the SAMBARA application. Because the Sambara application has a negative sentiment of 73.4%, it can be concluded that it still gets a bad rating in terms of use.
PENERAPAN ALGORITMA NAÏVE BAYES UNTUK PREDIKSI PENERIMAAN KARYAWAN Intan Murni Pratiwi; Ahmad Fauzi; Santi Arum Puspita Lestari; Yana Cahyana
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 1 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i1.1282

Abstract

The number of job seekers keeps growing, as does the quantity of companies that open job vacancies and offer opportunities to prospective employees. In terms of recruiting new employees, companies are very selective.. Companies are very selective in accepting prospective workers, where prospective workers must have qualifications that are in accordance with the positions needed in the company, because employees are an important asset in the growth and development of the company. because employees are an important factor in the growth and development of the company. Quality companies need good employees. This research uses employee recruitment data from PT Atma Darma Apta. The data has 372 rows and 8 attributes. The Naïve Bayes algorithm and the assessment techniques Mean Squared Error, Root Mean Squared Error, and R2 Score are used in this study. The results showed that the algorithm obtained good results by using a 90 to 10 data division resulting in a large accuracy value of 97.14%. In addition, the MSE, RMSE, and R2 Score values have quite good results, which are 2.86, 16.90, and 1.00. The 70 to 30 data division produces poor values with error values of 152.80 and 123.60, but the accuracy and R2 Score values are quite large at 96.15% and 0.95. With these results, this research can be continued into an application that can predict employee selection results.
Geometric Patterns in Jaipong Dance: An Ethnomathematics Study Lestari, Santi Arum Puspita; Kusumaningrum, Dwi Sulistya; Nurapriani, Fitria
Mathline : Jurnal Matematika dan Pendidikan Matematika Vol. 9 No. 1 (2024): Mathline: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/mathline.v9i1.556

Abstract

Jaipong dance is a traditional dance deeply rooted in the culture of West Java. However, not everyone is aware that Jaipong dance incorporates mathematical elements into its performance. Therefore, the aim of this research is to analyze mathematical concepts, particularly geometric patterns, within Jaipong dance. The research approach employed is ethnography, with data analysis including domain analysis, taxonomic analysis, and ethnographic analysis. Data was collected through three main methods: interviews, observations, and documentation. The research findings reveal the utilization of mathematical concepts in Jaipong dance. This includes counting from 1 to 8 to maintain the dance's rhythm and the use of geometric shapes in floor patterns. The floor patterns in Jaipong dance reflect the spatial arrangement used in the dance performance. Some of the floor patterns used in Jaipong dance encompass straight lines, diagonals, triangles, quadrilaterals, and pentagons. Thus, Jaipong dance not only blends artistic movements but also integrates mathematical and geometric concepts within its floor patterns. Geometry plays a significant role in creating visual aesthetics and regulating interactions among the dancers during Jaipong dance performances.
Co-Authors Adi Susilo Afga Ilyasa Ahmad Fauzi Ahmad Fauzi Anisa Itiawanti Baihaqi, Kiki Ahmad Banafshah Shafa Deden Wahiddin Dwi Sulistya Dwi Sulistya Kusumaningrum Faisal, Sutan Fauzi Ahmad Muda Firdaus, Thoriq Janati Firmansyah Maulana Hanny Hikmayanti Handayani Hartini, Dina Holila, Holila Indra, Jamaludin Intan Murni Pratiwi Iyan Khoerniyah Jamiluddin, Fathir Jorke Alkawiyu Juwita, Ayu Ratna Kusumaningrum, Dwi Sulistya Lutfi Aziz Maulana, Ikhsan Muhammad Ashi Dicky Niki Galih Prasetyo Nofie Prasetiyo Nurapriani, Fitria Nurmayanti, Trisya Nurofik Pratama, Adi Rizky Pratama, Rio Pratiwi, Sinta Amanda Rahmat Rahmat Ridwan, Ridwan Roby Rupiat Rohana, Tatang Siregar, Amril Mutoi Sutan Faisal Tohirin Al Mudzakir Wahiddin, Deden Wilda Amalia Yaman, Nuurul Izzati Yana Cahyana Yulia Ningsih