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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Journal of Telematics and Informatics INFOKAM Sisforma: Journal of Information Systems CESS (Journal of Computer Engineering, System and Science) Proceeding SENDI_U Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Rekam Medis dan Informasi Kesehatan Media Ilmu Kesehatan Jurnal Teknik Informatika UNIKA Santo Thomas Jesya (Jurnal Ekonomi dan Ekonomi Syariah) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Riset Informatika Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming SOSCIED Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Ilmiah Intech : Information Technology Journal of UMUS Tematik : Jurnal Teknologi Informasi Komunikasi TEPIAN Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Business and Technology J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat SENTRI: Jurnal Riset Ilmiah Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Seminar Nasional Ilmu Terapan Jurnal Kabar Masyarakat Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT Jurnal Sains dan Teknologi Informasi Journal of Future Artificial Intelligence and Technologies Proceeding of The International Conference on Mathematical Sciences, Natural Sciences, and Computing Jurnal Informatika Dan Tekonologi Komputer
Dian Kristiawan Nugroho
Universitas Stikubank
Aerospace Engineering Humanities Automotive Engineering 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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Penerapan Sistem Penilaian Kinerja : Dampaknya terhadap Peningkatan Kinerja Liana, Lie; Kasmari, Kasmari; Aquinia, Ajeng; Nugroho, Kristiawan
Jesya (Jurnal Ekonomi dan Ekonomi Syariah) Vol 7 No 1 (2024): Article Research Volume 7 Number 1, January 2024
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi Al-Washliyah Sibolga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36778/jesya.v7i1.1398

Abstract

Dosen adalah aset bagi sebuah perguruan tinggi, bahkan dosen disebut ujung tombak dalam proses pembelajaran. Hal ini menunjukkan bahwa dosen mempunyai peran yang vital dalam sebuah perguruan tinggi. Oleh sebab itu, penilaian kinerja terhadap dosen menjadi sangat penting untuk mengukur sistem tata kelola manajemen sumber daya manusia dalam rangka mencapai tujuan, visi dan misi perguruan tinggi. Berbagai perguruan tinggi terus mengembangkan sistem penilaian kinerja terhadap kinerja dosennya. Salah satunya adalah Universitas Stikubank Semarang, sebuah universitas yang berusia 55 tahun, tepatnya pada tanggal 28 April 2023. Data berupa data sekunder yaitu hasil penilaian kinerja yang telah dikumpulkan sejak tahun 2021 dan 2022. Responden dalam penelitian ini adalah 128 dosen. Dosen mengisi penilaian kinerja periode satu sampai April 2021. Berdasarkan hasil analisis didapatkan bahwa pada awal penilaian kinerja ini ada sejumlah 38,3% atau 49 dosen yang tidak memenuhi. Hal ini bisa diduga bahwa beberapa dosen belum siap untuk dinilai kinerjanya. Dengan adanya penilaian kinerja ini, para dosen seperti dibangunkan dari tidur panjangnya, bahwa situasi dan kondisi saat ini menuntut para dosen untuk melaksanakan tugas dengan baik. Pada penilaian kinerja periode Oktober 2021 ternyata dosen yang tidak memenuhi berkurang menjadi 12,5% atau 16 dosen. Ada usaha yang baik dari 33 dosen untuk memenuhi penilaian kinerjanya. Pada periode April 2022 ada 32,8% atau 42 dosen yang tidak memenuhi penilaian kinerja. Anehnya ada dosen yang sudah memenuhi di penilaian kinerja pada periode Oktober 2021 tetapi terkena di April 2022 yaitu ada 31 dosen. Tetapi usaha yang dilakukan oleh para dosen sungguh luar biasa. Pada bulan Oktober 2022 tinggal 3,9% atau 5 dosen yang tidak memenuhi penilaian kinerja. Berdasarkan hasil penelitian ini dapat disimpulkan bahwa penilaian kinerja telah berdampak pada peningkatan kinerja dosen di Universitas Stikubank Semarang.
Usability Sentiment Analysis Menggunakan Metode SUMI, NLP Scikit-Learn pada Aplikasi New Sakpole Aminudin, Agus; Hadiono, Kristophorus; Nugroho, Kristiawan
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 2 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i2.5451

Abstract

This research will discuss issues related to how to evaluate the usability and Sentiment Analysis aspects of the New Sakpole application system, how to determine the level of user satisfaction in using the New Sakpole mobile application and to determine sentiment analysis based on the results of analysis using the SUMI and NLP tools. The research objective is based on the formulation of existing problems to provide usability aspect values for the development of the New Sakpole mobile application and generate recommendations for improvement and determine the level of positive and negative sentiment analysis by using the New Sakpole Application as a medium for paying Motor Vehicle Tax. The test uses the Software Usability Measurement Inventory (SUMI) tool, the New Sakpole mobile application system, which is very helpful and can provide value to the community in the online vehicle tax payment process. This can be seen and obtained from a scale of helpfulness and efficiency resulting from a maximum score of 100 with an average score of 101 and 86.2. The results of the test using the SUMI tool, all average aspects get above average results, so the level of usability that occurs is that the use of New Sakpole has worked and is running well. The test uses Scikit-Learn Natural Language Processing (NLP) that the results of processing the review dataset on the New Sakpole Application from the Google Play Store with a total of 4704 reviews and a sampling of 500 reviews, that the response or reviews of the community using the New Sakpole application are negative even though for Acuracy word (words) that conveyed a review of 80.90%. From the results of the sample data test that index 0 is negative so that the words "good, very enlightening" can be concluded with Sentiment is 1 (POSITIVE)".
PENENTUAN PEMILIHAN VARITAS UNGGUL PADA TANAMAN PADI MENGGUNAKAN LOGIKA FUZZY TSUKAMOTO BERBASIS WEB Yoga Ryan Fatony; Kristiawan Nugroho
Elkom: Jurnal Elektronika dan Komputer Vol. 16 No. 2 (2023): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v16i2.1389

Abstract

Rice plants (Oryza sativa L.) are rice-producing plants which are a source of carbohydrates for most of the world's population. Almost 95% of Indonesia's population consumes rice as a staple food, so every year the demand for rice increases as the population increases. Therefore, farmers must choose quality seeds. In this era of fast and efficient technological progress, this is a very good thing for all progress in various fields. more and more fields of knowledge are developing, one of which is the existence of a decision-making system. a set of model-based procedures for processing and valuing data to help managers make decisions. This decision-making system uses several variables as input consisting of: type of variety, seed shape, seed color, root, seed age. The method used by the author is Fuzzy Tsukamoto. In the Tsukamoto method, it is explained that each consequence in IF-Then must be explained with a fuzzy set that has a membership function that does not change or is monotonous and for programming it uses PHP. The results obtained from the research that the authors conducted were in the form of a decision-making system to get the best seed yields.
Analysist of User Satisfaction of the High School Student Admissions Website using the User Experience Questionnaire Method Prabowo, Ardian Adi; Fathoni, Ahmad; Nugroho, Anjis Sapto; Nugroho, Kristiawan; Farooq, Omar
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 4 No. 1 (2025): March 2025
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v4i1.4841

Abstract

In the era of digitalization of public services, web-based student registration systems have become an important instrument in the education sector. The Website for New Student Admission (PPDB) of Senior High Schools (SMA) and Vocational High Schools (SMK) in Central Java Province has been implemented as a single platform for new student registration, but the main problem identified is the lack of a comprehensive evaluation of the level of user satisfaction with the quality of interaction experience with this platform, especially after the emergence of several complaints on the official PPDB social media regarding the system flow, services, and website appearance. The purpose of this study is to measure and analyze the level of user satisfaction with the PPDB website of SMA/SMK in Central Java Province using the User Experience Questionnaire (UEQ) approach which covers six aspects of user experience. This research method is descriptive quantitative with a survey approach using a standardized UEQ instrument consisting of 26 question items, involving 30 respondents of class X students of SMA Negeri 1 Karanganyar Demak selected using a 10% sampling technique from the population. The results of this study are indicate that the efficiency criteria obtained the highest score of 1.125, while the novelty criteria received the lowest score of 0.792, with the benchmark comparison diagram indicating a position below average (poor) in the criteria of attractiveness (1.061), clarity (1.092), accuracy (0.983), and stimulation (0.992), while in the criteria of efficiency and novelty, they are in a position above average (quite good). The implication of these findings underlines the need for further development in the aspects of visual appeal, clarity of information, accuracy of functions, and interaction stimulation to improve the overall quality of the user experience of the PPDB website.
Sistem Mobile Deteksi Gangguan Kejiwaan Berbasis Suara Menggunakan Metode Deep Convolutional Neural Network Nugroho, Kristiawan; Jusran, Alek; Sari, Linda Kartika; Nofiyanto, Muhamat; Suprihhartini, Suprihhartini
Jurnal Sains dan Teknologi Informasi Vol 5 No 1 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jussi.v5i1.8966

Abstract

-Mental disorders are a global health problem that often goes undetected early, requiring innovative approaches to their detection. This study aims to develop a mobile system capable of detecting mental disorders based on voice using Deep Convolutional Neural Network technology. The method used in this study is the collection of voice data from individuals experiencing symptoms of mental disorders, followed by voice feature extraction and the application of a Deep Convolutional Neural Network model for the classification of these disorders. The system was tested using a processed voice dataset, which includes various types of mental disorders, including depression and anxiety. The results showed that the Deep Convolutional Neural Network model was able to achieve high detection accuracy, with the ability to recognize mental disorders based on specific voice characteristics. This finding opens new opportunities for faster and more efficient detection of mental disorders using mobile devices, which are accessible to the wider community. This study also demonstrates the great potential of deep learning technology in the field of mental health, particularly in the prevention and diagnosis of mental disorders.
Sistem Rekomendasi Wisata di Pekalongan melalui Chatbot dengan Framework Rasa Fakhri; Kristiawan Nugroho
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 1 (2025): JANUARI-MARET 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i1.3000

Abstract

Pekalongan, a city renowned for its batik and rich in cultural and natural attractions, has great potential in the tourism sector. However, limited access to integrated and easily accessible information poses challenges for tourists planning their trips. The Rasa-based Telegram chatbot addresses these challenges as an innovative solution. Through interactive engagement, tourists can receive recommendations for destinations, culinary spots, and other relevant information tailored to their preferences. This system leverages Rasa Natural Language Understanding (NLU) to interpret user queries and provide appropriate responses. Comprehensive tourism data of Pekalongan is embedded into the system to ensure accurate and relevant recommendations. The chatbot's evaluation includes direct user testing to measure the accuracy of recommendations, user satisfaction, and ease of use. Results indicate that the Rasa-based Telegram chatbot can deliver personalized and accurate recommendations, enhancing the travel planning experience for tourists visiting Pekalongan.
Comprehensive Analysis and Classification of Skin Diseases based on Image Texture Features using K-Nearest Neighbors Algorithm Mamet Adil Araaf; Kristiawan Nugroho; De Rosal Ignatius Moses Setiadi
Journal of Computing Theories and Applications Vol. 1 No. 1 (2023): JCTA 1(1) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i1.9185

Abstract

Skin is the largest organ in humans, it functions as the outermost protector of the organs inside. Therefore, the skin is often attacked by various diseases, especially cancer. Skin cancer is divided into two, namely benign and malignant. Malignant has the potential to spread and increase the risk of death. Skin cancer detection traditionally involves time-consuming laboratory tests to determine malignancy or benignity. Therefore, there is a demand for computer-assisted diagnosis through image analysis to expedite disease identification and classification. This study proposes to use the K-nearest neighbor (KNN) classifier and Gray Level Co-occurrence Matrix (GLCM) to classify these two types of skin cancer. Apart from that, the average filter is also used for preprocessing. The analysis was carried out comprehensively by carrying out 480 experiments on the ISIC dataset. Dataset variations were also carried out using random sampling techniques to test on smaller datasets, where experiments were carried out on 3297, 1649, 825, and 210 images. Several KNN parameters, namely the number of neighbors (k)=1 and distance (d)=1 to 3 were tested at angles 0, 45, 90, and 135. Maximum accuracy results were 79.24%, 79.39%, 83.63%, and 100% for respectively 3297, 1649, 825, and 210. These findings show that the KNN method is more effective in working on smaller datasets, besides that the use of the average filter also has a significant contribution in increasing the accuracy.
Enhanced Vision Transformer and Transfer Learning Approach to Improve Rice Disease Recognition Rahadian Kristiyanto Rachman; De Rosal Ignatius Moses Setiadi; Ajib Susanto; Kristiawan Nugroho; Hussain Md Mehedul Islam
Journal of Computing Theories and Applications Vol. 1 No. 4 (2024): JCTA 1(4) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10459

Abstract

In the evolving landscape of agricultural technology, recognizing rice diseases through computational models is a critical challenge, predominantly addressed through Convolutional Neural Networks (CNN). However, the localized feature extraction of CNNs often falls short in complex scenarios, necessitating a shift towards models capable of global contextual understanding. Enter the Vision Transformer (ViT), a paradigm-shifting deep learning model that leverages a self-attention mechanism to transcend the limitations of CNNs by capturing image features in a comprehensive global context. This research embarks on an ambitious journey to refine and adapt the ViT Base(B) transfer learning model for the nuanced task of rice disease recognition. Through meticulous reconfiguration, layer augmentation, and hyperparameter tuning, the study tests the model's prowess across both balanced and imbalanced datasets, revealing its remarkable ability to outperform traditional CNN models, including VGG, MobileNet, and EfficientNet. The proposed ViT model not only achieved superior recall (0.9792), precision (0.9815), specificity (0.9938), f1-score (0.9791), and accuracy (0.9792) on challenging datasets but also established a new benchmark in rice disease recognition, underscoring its potential as a transformative tool in the agricultural domain. This work not only showcases the ViT model's superior performance and stability across diverse tasks and datasets but also illuminates its potential to revolutionize rice disease recognition, setting the stage for future explorations in agricultural AI applications.
Aspect-Based Sentiment Analysis on E-commerce Reviews using BiGRU and Bi-Directional Attention Flow De Rosal Ignatius Moses Setiadi; Warto Warto; Ahmad Rofiqul Muslikh; Kristiawan Nugroho; Achmad Nuruddin Safriandono
Journal of Computing Theories and Applications Vol. 2 No. 4 (2025): JCTA 2(4) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12376

Abstract

Aspect-based sentiment Analysis (ABSA) is vital in capturing customer opinions on specific e-commerce products and service attributes. This study proposes a hybrid deep learning model integrating Bi-Directional Gated Recurrent Units (BiGRU) and Bi-Directional Attention Flow (BiDAF) to perform aspect-level sentiment classification. BiGRU captures sequential dependencies, while BiDAF enhances attention by focusing on sentiment-relevant segments. The model is trained on an Amazon review dataset with preprocessing steps, including emoji handling, slang normalization, and lemmatization. It achieves a peak training accuracy of 99.78% at epoch 138 with early stopping. The model delivers a strong performance on the Amazon test set across four key aspects: price, quality, service, and delivery, with F1 scores ranging from 0.90 to 0.92. The model was also evaluated on the SemEval 2014 ABSA dataset to assess generalizability. Results on the restaurant domain achieved an F1-score of 88.78% and 83.66% on the laptop domain, outperforming several state-of-the-art baselines. These findings confirm the effectiveness of the BiGRU-BiDAF architecture in modeling aspect-specific sentiment across diverse domains.
ANALISIS KOMPARATIF NILAI PASAR DAN PERFORMA PEMAIN: IDENTIFIKASI PEMAIN UNDERVALUED BERBASIS BIG DATA ANALYTIC Lintang Amarul Fatah; Hanacahyani Widya Asih; Mukti Diananingsih; Djoko Pitoyo; Kristiawan Nugroho; Eka Ardhianto
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7460

Abstract

The discrepancy between market value and actual player performance often creates financial inefficiencies in football club recruitment strategies. This study aims to identify undervalued players (high performance but low valuation) using Big Data Analytics on the Transfermarkt dataset. The initial dataset is large-scale, comprising 32,601 player records and 1,706,806 match appearance entries, reflecting high-volume data characteristics The research methodology follows four systematic stages: (1) massive data acquisition and integration covering match statistics and transfer history; (2) implementation of Feature Engineering to convert raw statistics into per-90-minute metrics while accounting for contract duration; (3) fair value modeling using the CatBoost Regressor algorithm optimized with Log-Transformation to handle skewed data distributions; and (4) model validation using 5-Fold Cross Validation and residual analysis to detect price anomalies. The results demonstrate the model's ability to precisely identify potential player segments overlooked by standard market valuations. It is concluded that integrating CatBoost with robust feature engineering serves as a strategic instrument for club management to enhance investment efficiency (Return on Investment). 
Co-Authors Ach. Ridlo Bayu Ajie Achmad Nuruddin Safriandono Afandi , Afandi Afif, Randi Ahmad Fathoni Ajib Susanto Ajie, Ach. Ridlo Bayu Alex Chandra Iswanto Amat Wiratno Aminudin, Agus Anjis Sapto Nugroho Anton Sujarwo Anton Sujarwo Aprico, Fikky Apriyanti, Dewi Aquinia, Ajeng Arsyad , Muhammad Rafi Haidar budi hartono Budiarto, Indri Cahaya, Agus Indra Cahyono Rahadiyanto De Rosal Ignatius Moses Setiadi Dhendra Marutho Dian Palupi Djoko Pitoyo Dwi Agus Diartono Dwi Budi Santoso Edy Winarno Eka Ardhianto Eko Prasetyo Eko Prasetyo Eksawati, Rini Endang Tjahjaningsih Eri Zuliarso Ermillian, Ade Faizi, Aditya Wahyu Nur Fajar Raharjo fakhri Fakhri Farooq, Omar Hanacahyani Widya Asih Hari Murti Haris Asso Heni Candra Kirana Heribertus Yulianton Hermawan, Taufan Hidayat, Suluh Himawan Wicaksono Hussain Md Mehedul Islam Isworo Nugroho Jusran, Alek Kasmari . Kirana, Heni Candra Kristhoporus Hadiono Kristianto, Taufik Fredy Kristophorus Hadiono Lie Liana Lie Liana . Linda Kartika Sari Lindu Fitrianto Lintang Amarul Fatah Mamet Adil Araaf Minantri Haika, Shara Muh Kholid Rizky Sapawi Muhamad Riski Atarik Mukti Diananingsih Mulyani , Wahyu Sri Mulyo Budi Setiawan Munna, Aliyatul Muslikh, Ahmad Rofiqul Niken Puspitasari Nofiyanto, Muhamat Nurmakhlufi, Alfin Ojugo, Arnold Adimabua Omar Farooq Omar Farooq Palupi, Dian Perdana, Willy Yudha Prabowo, Ardian Adi Prihatin, Rudi Setyo R.M.Herdian Bhakti Radyanto, Mohammad Riza Rahadian Kristiyanto Rachman Raharjo, Fajar Retnowati Rini Eksawati Rokhayadi, Wakhid Ruslana, Zauyik Nana Sandy Irawan Saputra, Roni Halim Saputro, Risky Wisnu Sariyun Naja Anwar Sarwo Edi, Sarwo Setyaningtyas, Elvanita Siti Sholihah Ari Susanti Sri Mulyani Sugeng Murdowo Suhana Suhana Sulastri Sulastri Sulistiyowati Sulistiyowati Suluh Hidayat Sunardi Sunardi Suprihhartini, Suprihhartini SUTANTO, FELIX Syahroni Wahyu Iriananda, Syahroni Wahyu Taufik Fredy Kristianto Teguh Khristianto Veronica Lusiana Vici Tiara Anjarsari Warto Wendhie Tri Wijayanto Widiyanto Tri Handoko Wijayanto, Wendhie Tri Wiratno, Amat Wismarini , Th. Dwiati Wiwien Hadi Kurniawati Yayi Suryo Prabandari Yoga Ryan Fatony Yoga Ryan Fatony Yossy Suprapto