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All Journal Jurnal Buana Informatika CommIT (Communication & Information Technology) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah FIFO Jurnal Teknoinfo Jurnal Teknologi Sistem Informasi dan Aplikasi CYBERNETICS Jurnal Riset Informatika JOISIE (Journal Of Information Systems And Informatics Engineering) Progresif: Jurnal Ilmiah Komputer TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Jurnal Teknologi Informasi dan Multimedia JURNAL TEKNIK Jurnal ABDINUS : Jurnal Pengabdian Nusantara Jurnal Teknik Informatika C.I.T. Medicom JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JIKA (Jurnal Informatika) Journal of Management and Bussines (JOMB) Jurnal Inovasi Informatika Idealis : Indonesia Journal Information System Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Prosiding Konferensi Nasional PKM-CSR Joutica : Journal of Informatic Unisla Jurnal Komputer, Informasi dan Teknologi
Mayatopani, Hendra
Universitas Pradita
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering 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 Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Nursing Public Health Social Sciences

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EXPERT SYSTEM FOR DIAGNOSING BEHAVIORAL DISORDERS USING THE DEMPSTER-SHAFER THEORY ALGORITHM Mayatopani, Hendra
TEKNOKOM Vol. 7 No. 1 (2024): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v7i1.181

Abstract

Behavioral disorders, especially in children, can have a significant impact on their development in various domains, such as social, emotional, and academic. The diagnostic process for this disorder can be complicated due to overlapping symptoms and the possibility of complex causal factors. Early identification and appropriate treatment of behavioral disorders is essential to prevent more serious impacts on an individual's psychological well-being. Lack of knowledge and difficulty in accessing psychiatrists to find out about behavioral disorders in children are factors that result in this problem requiring a solution. The aim of this research is to create an expert system that utilizes the Dempster-Shafer Theory algorithm to detect behavioral disorders, thus simplifying the diagnosis process and ensuring accurate results. The Dempster-Shafer theory, as an inference engine, can overcome uncertainty by combining several sources of evidence or data that may overlap or be incomplete, resulting in a stronger conclusion. The main feature of this expert system is its ability to carry out diagnoses based on symptoms and display diagnosis results, disease descriptions, and treatment options. Test accuracy produces a value of 90%, which shows that the Dempster-Shafer Theory approach can diagnose behavioral disorders effectively.
Analisis Perbandingan Algoritma Load Balancing Source Hash Scheduling dan URI Berdasarkan Throughput Pada Server Web Mayatopani, Hendra; Herdiansah, Arief; Sofyan, Sofyan; Kaaffah, Faiz Muqorrir
Jurnal Ilmiah FIFO Vol 16, No 2 (2024)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2024.v16i2.008

Abstract

Seiring dengan pesatnya perkembangan internet, terjadi peningkatan signifikan dalam jumlah pengguna yang terhubung, yang berdampak langsung pada kebutuhan performa server web. Penelitian ini bertujuan untuk mengevaluasi kinerja server web melalui penerapan metode Load Balancing menggunakan dua algoritma berbeda, yaitu Source Hash Scheduling (SHS) dan Uniform Resource Identifier (URI). Metode Load Balancing dipilih karena kemampuannya dalam mendistribusikan beban kerja secara merata di antara beberapa server, sehingga dapat meningkatkan efisiensi dan ketersediaan sistem. Algoritma SHS digunakan karena kemampuannya dalam memastikan konsistensi distribusi permintaan berdasarkan alamat IP sumber, sementara algoritma URI dipilih karena dapat mendistribusikan permintaan berdasarkan pola URI yang lebih spesifik. Uji coba dilakukan untuk mengukur efektivitas kedua algoritma dalam meningkatkan throughput pada berbagai tingkat koneksi. Hasil analisis menunjukkan bahwa kedua algoritma memberikan performa yang sangat baik dan konsisten. Pada koneksi rendah (1000/100), keduanya mencatat throughput identik sebesar 51.20 KB/s. Namun, pada tingkat koneksi lebih tinggi (2000/200 hingga 5000/500), URI sedikit lebih unggul dengan throughput hingga 255.70 KB/s, dibandingkan dengan 255.74 KB/s pada SHS. Meskipun perbedaan performa sangat kecil, URI menunjukkan stabilitas yang lebih baik dalam menjaga throughput. Oleh karena itu, pemilihan algoritma dapat disesuaikan dengan kebutuhan spesifik terkait stabilitas atau performa pada tingkat koneksi tertentu, dengan kedua algoritma ini menawarkan solusi andal untuk meningkatkan kinerja dan kepuasan pengguna server web.
A COMPARISON OF THE NAIVE BAYES AND K-NN ALGORITHMS IN PREDICTING THE FRESHNESS OF MILKFISH AT FISH AUCTIONS Setiyowati, Harlis; Mayatopani, Hendra; Hariyanto, Lilik; Harriz, Muhammad Alfathan
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2277

Abstract

This research aims to compare the performance of two machine learning algorithms, Naive Bayes and K-Nearest Neighbors (K-NN), in predicting the freshness of milkfish (Chanos chanos) at fish auctions. Predicting fish freshness is an important aspect to ensure product quality and customer satisfaction. The Naive Bayes algorithm, which is based on Bayes' Theorem with the assumption of independence between features, as well as the K-NN algorithm, which uses an instance-based approach to classify data based on proximity to k nearest neighbors, were implemented and tested. Evaluation is carried out using accuracy and Kappa metrics. The results show that Naive Bayes achieved an accuracy of 73.44% with a Kappa value of 0.594, indicating good performance in predicting the freshness of milkfish. In contrast, K-NN shows an accuracy of 68.75% and a Kappa value of 0.461, which means its performance is lower compared to Naive Bayes. Further analysis revealed that Naive Bayes is more computationally efficient and faster at making predictions, making it better suited for real-time applications at fish auctions. However, Naive Bayes has limitations in assuming feature independence which may not always be true in real-world situations. On the other hand, K-NN although more flexible and capable of capturing complex patterns in data, tends to be slow and requires optimization of parameters such as k values ​​to improve its performance. In conclusion, Naive Bayes is recommended for predicting the freshness of milkfish at fish auctions because of its higher accuracy and reliability. Further research is needed to optimize these two algorithms through parameter adjustments and the use of ensemble methods to improve overall prediction performance.
SENTIMENT ANALYSIS PUBLIC PERSPECTION FROM ARTEMIS 2 MISSION USING RECURRENT NEURAL NETWORK METHODS Muhammad Agym; Mayatopani, Hendra
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2365

Abstract

This research underlies a deep sentimental analysis of NASA's Artemis 2 project, which aims to bring humans back to the Moon. This mission is an important milestone in NASA's efforts to build a long-term human presence on the Moon. In the context of this large and controversial project, the sentimental analysis carried out using the Recurrent Neural Network method is aimed at understanding the public view. This sentimental analysis provides a better understanding of how the public responds or perceives the Artemis 2 mission. The research questions focused on the methods of repetitive nerve tissue performance in classifying public sentiment towards the Artemis mission. The results of sentimental analysis show a strong positive trend, providing support for the continuity and sustainability of the project. From the data obtained and processed, the majority of respondents expressed a positive view of the Artemis 2 mission. Of the 49 respondents, 77.6% had a positive sentiment, 10.2% were neutral, and 12.2% were negative. The findings describe public support for the mission as a step forward in space exploration and scientific research. Nevertheless, it is important to interpret the results carefully and take into account cultural and political contextual factors. Research advice includes integrating sentimental analysis with active public participation, dealing with ethical and privacy issues, and specific analysis of specific demographic groups. The research is expected to provide in-depth insight into how people respond to space exploration, benefiting the development of sentimental analysis models, public involvement, and an understanding of social and cultural impacts.
Evaluasi Usability Aplikasi Ibis Paint X Menggunkan Metode System Usability Scale dan User Experience Questionnaire Asmasudirdja, Amalia Zaini; Mayatopani, Hendra
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 6 No. 4 (2023): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

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

Abstract

Ibis Paint X is a drawing application that is quite in demand, because it has been downloaded by more than 100 million users with around 1.9 million reviews on the Google Play Store. Until now, there has been no research on the usability evaluation of  the  Ibis Paint X application, so there may be some unknown usability problems. This study aims to evaluate  the usability of  Ibis Paint X as a digital drawing application that is quite in demand. In evaluating the usability of Ibis Paint X, two usability measurement methods are used, namely the System Usability Scale (SUS) and the User Experience Questionnaire (UEQ). The purpose of using two methods to evaluate  the usability of the Ibis Paint X application is to obtain more in-depth evaluation results from two points of view of the usability evaluation method of an application. The evaluation process was assessed by 122 respondents using the Ibis Paint X application. Based on the evaluation results of the SUS method getting a score of 68.69, judging from the interpretation of the SUS score which has 4 categories, it can be concluded that Ibis Paint X gets a "C" predicate in the grade scale category, is considered "OK" in the adjectives rating category, including "Marginal" in the category  acceptability ranges, and have users who are "Passive" in the NPS category. For the evaluation results, the UEQ method has positive results as a digital drawing application because each category of UEQ methods, namely attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty, has an average value of more than 0.8.
Penggunaan Metode AHP dalam Menentukan Cryptocurrency untuk Investasi Farrell Ivander Daviano Siwy; Malvin Setiadi Dharmawan; Hendra Mayatopani
Jurnal Teknologi Informasi dan Multimedia Vol. 5 No. 2 (2023): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i2.321

Abstract

The use of digital currency or cryptocurrencies has been common since 2018. However, its use as an investment instrument is different from the conventional currency recognized by the state, this is because a cryptocurrency appeared by a number of organizations/companies and its value does not depend on the economic condition of a country, but on the status of a company/issuer of a digital currency. Difficulty predicting liability and growth creates computational needs to determine which digital currencies are suitable for investment, so in this study, 11 digital currencies are used to try out. Experiment with AHP calculations to get the best digital currency to use as an investment tool. This calculation translates into IOT Coin against other famous digital currencies, such as Bitcoin or Ethereum.
Pengembangan Computational Thinking Melalui IoT Apps Programming Dengan Tinkercad Rochadiani, Theresia Herlina; Santoso, Handri; Mayatopani, Hendra
Jurnal ABDINUS : Jurnal Pengabdian Nusantara Vol 6 No 1 (2022): Volume 6 Nomor 1 Tahun 2022
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/ja.v6i1.16007

Abstract

Computational thinking is one of competencies are needed recently. Most countries had already included computational thinking into their curriculum. Based on PISA 2018, Indonesia placed 72nd ranking of 77 countries for reading, 72nd ranking of 78 countries for mathematics, and 70th ranking of 78 countries for sciences. It is of concern to government and us to elevate this computational thinking ability, especially for students. Through this community service, the training of IoT programming using Tinkercad is given to PAHOA Senior High School students. Theory and hands-on practical in this training was followed by 40 students for 4 months. Based on questionnaire in the end of this training, 62% participations agreed that their computational thinking increase through this training and 96% participations could make IoT apps.
Sentiment Analysis of Indonesian Society Toward the Launch of iPhone 16 Using Naive Bayes, Random Forest, and KNN Algorithms Christopher Ezra Manurung; Hendra Mayatopani
Jurnal Komputer, Informasi dan Teknologi Vol. 5 No. 1 (2025): Juni
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v5i1.2219

Abstract

The development of smartphone technology, especially involving global brands like Apple, always attracts the attention of the world, including Indonesia. Every time Apple launches a new product, the public's response, particularly in Indonesia, often appears in the form of tweets on the social media platform Twitter, now known as X, which can be analyzed to reflect public views. This phenomenon presents an opportunity to understand how products are received in today's market. The dataset used in this study was obtained from tweets or comments from the Indonesian public between October and November 2024. The study found that 51.49% of the tweets fell into the positive sentiment category, 28.15% were neutral, and 20.35% were negative. Accuracy evaluation using three algorithms showed that Random Forest had the highest accuracy at 72.4%, followed by KNN with an accuracy of 66.9%, and Naïve Bayes with an accuracy of 66.3%. The results of this study indicate that the majority of the Indonesian public showed a positive sentiment toward the launch of the iPhone 16, reflecting high enthusiasm for the product. Furthermore, the Random Forest algorithm proved to be more effective in sentiment classification  compared to KNN and Naïve Bayes, with higher accuracy.
CLASSIFICATION OF VEHICLE TYPES USING BACKPROPAGATION NEURAL NETWORKS WITH METRIC AND ECCENTRICITY PARAMETERS Mayatopani, Hendra; Borman, Rohmat Indra; Atmojo, Wahyu Tisno; Arisantoso, Arisantoso
Jurnal Riset Informatika Vol. 4 No. 1 (2021): December 2021
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (758.834 KB) | DOI: 10.34288/jri.v4i1.139

Abstract

One of the efforts to break down traffic jams is to establish special lanes that can be passed by two, four, or more wheeled vehicles. By being able to recognize the type of vehicle can reduce congestion. Citran based vehicle classification helps in providing information about the vehicle type. This study aims to classify the type of vehicle using a backpropagation neural network algorithm. The vehicle image can be recognized based on its shape, then the backpropagation neural network algorithm will be supported by metric and eccentricity parameters to perform feature extraction. Then from the results of feature extraction with metric parameters and eccentricity, the object will be classified using a backpropagation neural network algorithm. The test results show an accuracy of 87.5%. This shows the algorithm can perform classification well.
Co-Authors Alamsyah, Dedy Arisantoso Arisantoso Asmasudirdja, Amalia Zaini Atmojo, Wahyu Tisno Audrey, Kelly Kirsten Christina Ratih Oktaviani Christopher Christopher Christopher Ezra Manurung Christopher Ezra Manurung Damayanti, Helen Agustin Puspa Dazki, Erick Deasy Olivia Deasy Olivia, Deasy Destriana, Rachmat Dian Adi Prastiyo Edison Siregar, Master Faiz Muqorrir Kaaffah Fandhilah Fandhilah Farel Ivander Daviano Siwy Farrell Ivander Daviano Siwy Gupita Sari, Marchelia Harlis Setiyowati Herdiansah, Arief Heri Kurniawan, Heri Indra Sudarsono Malvin Setiadi Dharmawan March Vircan Karuna Master Edison Mochamad Sanwasih Muhammad Agym Muhammad Bagir Narindro, Laser Nofitri Hariyani Nofitri Heriyani Nunik Yudaningsih Nurdiana Handayani Nurdiana Handayani Nurdiana Handayani Nurdiana Handayani Prathama, Dhion Angga Ri Sabti Septarini Rico, Rico Rido Dwi Kurniawan Rini Nuraini Rino Subekti Riskita Paramitha Riyanto, Umbar Rochadiani, Theresia Herlina Rohmat Indra Borman Rohmat Taufiq Santoso, Handri Siregar, Master Edison Sofyan Tanuwijaya, Piter V. Lilik Hariyanto Warjiyono Warjiyono Yanuar Eka Ramadhan Yoga D Putra Musli