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All Journal Sainteks Prosiding Semnastek Semesta Teknika JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Ekonomi Jutech: Jurnal Teknologi Informasi Jurnal Sistem Informasi
Vany Terisia
Institut Teknologi Dan Bisnis Ahmad Dahlan, Jakarta
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mechanical Engineering Medicine & Pharmacology Social Sciences

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RANCANG BANGUN SISTEM INFORMASI E-LEARNING BERBASIS WEB (STUDI KASUS MI AL-INAYAH DARMINIYAH) Indri, Indri Mulyani; Vany Terisia; Muhajir Syamsu
Jurnal Sistem Informasi (JUSIN) Vol 4 No 2 (2023): Jurnal Sistem Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jusin.v4i2.2092

Abstract

Learning is a process of interaction between educators and students where educators provide knowledge to grow at once develop insight, creativity and mindset of students. website-based e-learning to support the learning process teaching at MI Al-Inayah Darminiyah which can make it easier for students to learn and provide flexibility to be able to receive learning materials where and anytime without face-to-face contact with the teacher. The method used is the method and UML (Unified Modeling Language) and built using the PHP programming language (Hypertext Preprocessor) and using MySQL as database server as well XAMPP as a web server. Data collection through interviews with teachers and school staff as well as make observations which are then carried out by research literature. The result is that the e-learning website can be used by the admin, teachers and students at MI Al-Inayah Darminiyah in supporting the learning process teaching at the moment. Admins can manage teacher and student accounts, teachers can easily manage the material and assignments submitted. Student with easily get school notifications as well as materials and assignments delivered by the teacher. This e-learning website helps schools to improve processes learning optimally so that it can further affect improvement academic student achievement because of the convenience for students in accessing the media learning anytime and anywhere in accordance with the school's vision of " Menjadi Sekolah yang Berprestasi dan Berakhlakul Karimah”.
Penerapan Algoritma Backpropagation Untuk Memprediksi Jumlah Pasien Pra-Kanker Serviks (Studi Kasus Di Puskesmas Padang Pasir) Arman, Shevti Arbekti; Terisia, Vany; Gumelar, R. Tommy
Jurnal Teknologi Informasi (JUTECH) Vol 1 No 1 (2020): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v1i1.847

Abstract

According to the Global Cancer Observatory report in 2018, Indonesia ranks second in the largest cervical cancer cases in the world. 80% of cervical cancer sufferers come in an advanced stage, and 94% of patients of these cases die within 2 years. Preventive action from the government is expected to reduce the number of sufferers. By knowing the increase in the number of patients, the government can take action on what must be done to reduce the number of pre-cervical cancer patients. Data on the number of patients with cervical pre-cancer is continuous, so the method needed to make predictions is a complex method and study the uncertainties in each period that can be accommodated with the Artificial Neural Network (ANN) Backpropagation algorithm. Backpropagation architecture used is 5 input layers, 5 hidden layers, and 1 output layer, with a learning rate (lr) of 0.5; constant momentum (mc) 0.3; the Mean Square Error (MSE) value of network training is 0.001 with the logsig activation function for hidden layers and purelin for the output layer. Resulting in a 5-5-1 network architecture pattern with the epoch = 322 processes and the achievement of MSE when testing with MSE = 0.001 with an accuracy of 99.999%.
Perancangan Aplikasi Data Mining Menggunakan Association Rule Dengan Metode Algoritma Apriori Untuk Analisis Market Basket (Studi Kasus Pada Tesco Swalayan) Arman, Shevti Arbekti; Terisia, Vany; Toha, Muhamad
Jurnal Teknologi Informasi (JUTECH) Vol 2 No 2 (2021): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v2i2.1659

Abstract

The data contained ranging from business transactions to government is very large in size so we need a system that can sort and select the data. Intense competition in the business world, especially in the food industry or supermarkets, requires developers to find strategies that can increase sales. Data mining is used to find patterns and regularities in data sets that are hidden by using technology. In knowing the products purchased by consumers, developers can use market basket analysis, namely an analysis of consumer buying habits and then detect products that consumers often buy simultaneously using association rules. Search for associations or relationships between items taken from a relational database using an a priori algorithm to form candidate item combinations and then tested whether the combination meets the minimum support and confidence parameters which are the threshold values ​​given by the user. Furthermore, it is applied by performing query processing, decision making and so on. Data mining applications with the association rule method have been able to perform calculations correctly as expected. The knowledge generated by the application and manual calculations is 17.2% of all transactions, granulated sugar and pomegranate cooking oil are purchased simultaneously. And of all transactions that buy granulated sugar, 55.5% will buy Pomegranate Cooking Oil. 17.2% of all transactions, Pomegranate Cooking Oil and Granulated Sugar are purchased simultaneously. And of all transactions that buy Pomegranate Cooking Oil, 55.5% will buy granulated sugar.
Mengiplementasikan Vector Space Model Similarity Euclidean Distance Menggunakan TFIDF Pada Klasifikasi Teks Bahasa Indonesia Fitriansyah, Reza; Sestri, Ellya; Terisia, Vany
Jurnal Teknologi Informasi (JUTECH) Vol 3 No 2 (2022): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v3i2.2034

Abstract

Weighting based on the term with stemming techniques to get the basic word form term in question. This will the application of the Indonesian language text classification machine using the K-Nearest Neighbor algorithm and the Vector Space Model method on the TFIDF frequency weighting of the number of words and the Euclidean Distance function. comparison between the test documents and the test sample collection Using news documents as learning documents, a total of 10 (10) documents with 3 (three) categories, produces an Precision and Recall 90.00% for k = 5 using frequency weighting in words with the Euclidean Distance function.
Penerapan Algoritma C4.5 Dalam Penilaian Kepuasan Pelanggan Terhadap Layanan (Studi Kasus: Nilla Wedding Gallery) Riska Sulistiani; Diana Yusuf; Vany Terisia
Jurnal Teknologi Informasi (JUTECH) Vol 4 No 2 (2023): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v4i2.2160

Abstract

Nilla Wedding Gallery is a service provider engaged in cosmetology and wedding management. During the run, existing customer data has never been managed optimally only limited to piles of data and has never used the data mining process to find patterns or information from service sales data to customers. Data Mining is a technique that processes or manages data into information. In this study the author uses the C4.5 algorithm method, the C4.5 method is used as an approach to generate a prediction model based on attributes relevant to customer satisfaction. Data obtained from a case study at Nilla Wedding Gallery was used to train and test the C4.5 model. As a form of this effectiveness, the author built a web mining system using the PHP programming language. The results showed that the C4.5 algorithm is effective in identifying factors that affect customer satisfaction, and is able to produce decisions that are beneficial for policy making by Nilla Wedding Gallery in improving service quality and achieving higher customer satisfaction.
Penerapan Data Mining Untuk Menentukan Jenis Asuransi Yang Paling Diminati Di PT AIA Financial Dengan Metode Clustering Fahrul Razi; Vany Terisia; Diana Yusuf
Jurnal Teknologi Informasi (JUTECH) Vol 4 No 1 (2023): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v4i1.2345

Abstract

Finding information that is concealed in databases is referred to as data mining. Data mining is a process used to extract and find relevant information and related knowledge from numerous very big datasets using statistical, mathematical, artificial intelligence, and machine learning approaches. The steps involved in the data mining process start with the selection of data from the source data to the target data, followed by preprocessing to enhance the quality of the data, transformation, data mining, and stages of interpretation and assessment that result in new information. Organizing data sets into many groups through the process of clustering ensures that objects in one group share a lot in common and differ greatly from those in other groups. Data mining technique K-Means Clustering divides data into one or more clusters. RapidMiner is the program used in this data mining application, which uses the Knowledge Discovery in Databases (KDD) stage. According to this report, AIA Family First Protection, AIA Prolink, and AIA Life Secure are the three most popular insurance products. With the use of this research, PT. AIA Financial will be able to identify the most popular insurance product categories and market these products to the general public.
Pengembangan Jaringan Switching Menggunakan Metode QinQ (Studi Kasus: PT. Artajasa Pembayaran Elektronis) Pradanni Kresna Mukti; Muhajir Syamsu; Vany Terisia
Jurnal Teknologi Informasi (JUTECH) Vol 4 No 1 (2023): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v4i1.2347

Abstract

PT. Artajasa Pembayaran Elektronis is an electronic payment service provider company, which is one of the companies that utilizes the reliability of connectivity in the network, to support the communication of each employee owned. Focused by the author to conduct research aimed at developing the switching network of Artajasa using QinQ method. This method is implemented to facilitate the infrastructure team and network engineers Artajasa, in managing and management of active VLAN ID owned, namely by wrapping the existing VLAN ID using an outer-vlan, QinQ method allows Artajasa when there is a need to add a new VLAN, simply add it on one of the distribution switches only (BSD-SW-IN), configuration using QinQ method is different from when Artajasa uses the trunk allowed vlan method that requires adding one by one VLAN ID on the distribution switch. In the implementation of the author in developing Artajasa switching network, testing is carried out with parameters that are in accordance with QoS standards. After doing the test, get the value of 21ms when using the QinQ method on the Delay test, better than when not using the QinQ method. Packet loss test result showing 0.005% when using the trunk allowed vlan method, after using QinQ packet loss reduce to 0%. QinQ method also affects the results throughput test is 80.64% compared without using the QinQ method that only get a 64.31% throughput presentation.
Perancangan Lampu Otomatis Menggunakan Sensor Cahaya Dan Timer Berbasis Arduino Uno Prita Niken Puspita; Muhajir Syamsu; Vany Terisia
Jurnal Teknologi Informasi (JUTECH) Vol 4 No 1 (2023): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v4i1.2348

Abstract

People's lives are busy with activities outside the house, often crushing small things like turning the lights off, doing outdoor activities until late at night, dark housing conditions are very potentially criminal. The technology used to solve such problems is something of an automatic nature. Automatic technology is a home light that can turn on and off automatically. The method used is a qualitative method that has descriptive properties, i.e. the researcher describes a phenomenon with accurate data and is systematically studied. Automatic light design with a light sensor that inserts a program in an Arduino uno module that can give instructions of light on and off. The light sensor connected to the relay as an automatic switch is heavily dependent on the light resistance value received by the light sensor. When the light sensor receives less than 200, the light goes off. When at night the resistance values received by a light sensor are more than 200 then the light is automatically lit. The automatic light design uses the Light Dependent Resistor (LDR) light sensor module that can automatically turn off the light through the resistant value received from the light Sensor, and can use the timer as a form of system development when the light becomes uncertain and occurs in the rainy season. A timer can direct the schedule to turn on and off the lights as needed.
Penerapan Model Infrastruktur Artificial Intelligence Sebagai Penggerak Industri 4.0 Syamsu, Muhajir; Terisia, Vany; Yusuf, Diana
Jurnal Teknologi Informasi (JUTECH) Vol 3 No 1 (2022): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v3i1.2375

Abstract

Artificial Intelligence (AI) memainkan peran yang sangat penting dalam mendorong Industri 4.0. yang mengacu pada transformasi industri yang didorong oleh teknologi digital, termasuk AI, Internet of Things (IoT), data besar, robotika, dan komputasi awan. AI memberikan kemampuan komputasi dan otomatisasi cerdas yang dapat meningkatkan efisiensi, produktivitas, dan inovasi di berbagai sektor industri, dengan berfokus pada pengembangan sistem komputer yang mampu melakukan tugas-tugas yang membutuhkan kecerdasan manusia, di mana AI melibatkan pembuatan mesin yang dapat belajar, merencanakan, beradaptasi, dan melakukan tugas-tugas cerdas seperti pengambilan keputusan, pengenalan suara atau gambar, pemrosesan bahasa alami, dan pemecahan masalah. Penelitian ini bertujuan untuk menyelidiki penerapan Model Infrastruktur Artificial Intelligence (AI) sebagai pendorong Industri 4.0. Penelitian ini akan melibatkan pengembangan model AI yang dapat digunakan dalam konteks industri modern untuk meningkatkan efisiensi, produktivitas, dan inovasi. Dengan menggunakan metode analisis kebutuhan, metode ini melibatkan analisis mendalam terhadap target industri, baik dari segi infrastruktur maupun kebutuhan yang harus dipenuhi. Melalui wawancara, observasi, dan analisis data, peneliti mampu mengidentifikasi area-area di mana teknologi AI dapat memberikan dampak dan solusi tepat guna yang mampu memberikan manfaat signifikan dalam meningkatkan efisiensi, produktivitas, kualitas, dan inovasi di industri, dengan implementasi yang kuat terhadap kebutuhan industri, pengumpulan dan pengolahan data yang baik, adaptasi dengan konteks dan peraturan yang berlaku untuk Industri 4.0 di perusahaan.
Penyebaran Mahasiswa ITB Ahmad Dahlan Jakarta Menggunakan Algoritma Clustering Toha, Muhamad Toha; Diana Yusuf; Vany Terisia
Jurnal Teknologi Informasi (JUTECH) Vol 5 No 1 (2024): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v5i1.2450

Abstract

The development of information technology has made it possible to collect and analyze large and complex data. In the context of higher education, information about student distribution patterns can be of added value for institutions to plan and manage their resources effectively. This research aims to design a web mining system that uses a clustering algorithm to grouping the distribution of students at ITB Ahmad Dahlan. In this study, we collected data from open web sources that are relevant to students, such as student profiles, academic preferences, and extracurricular activities. The data is then analyzed using a clustering algorithm to identify patterns and trends in student distribution. By using this approach, we hope to provide useful insights for educational institutions in planning infrastructure, academic programs, and student services.
Co-Authors Adhitio Satyo Bayangkari Karno Akmal Annafis Cakranegara, Pandu Adi Daruningsih, Kukuh Diana Yusuf Diana Yusuf Dodi Arif Elliya Sestri Fahrul Razi Fahrul Razi Fauzan, Alif Raihanza Fiqhri, Zul Gumelar, R. Tommy Indri, Indri Mulyani Karno, Adhitio Satyo Bayangkari Lyscha Novitasari Muhajir Syamsu Muhajir Syamsu Muhammad Ilham Pakawaru Nada Kamilia Nurjayanti , Revi Pradanni Kresna Mukti Prita Niken Puspita Putra, Yoga Rarasto R. Tommy Gumelar Raihan, Dendi Reza Fitriansyah Riska Sulistiani Sestri, Elliya Sestri, Ellya Shevti Arbekti Arman Silvia Ningsih Stefani Lily Indarto Sudarwanto, Pantja Syamsu, Muhajir Syehand Aby Riano Tanti Widia Nurdiani Toha, Muhamad Toha, Muhamad Toha Widi Hastomo YUSUF, DIANA