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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TEKNIK INFORMATIKA Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Proceeding of the Electrical Engineering Computer Science and Informatics Fountain of Informatics Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Indonesian Journal on Software Engineering (IJSE) Faktor Exacta Jukung (Jurnal Teknik Lingkungan) CogITo Smart Journal INOVTEK Polbeng - Seri Informatika JRMSI - Jurnal Riset Manajemen Sains Indonesia KACANEGARA Jurnal Pengabdian pada Masyarakat Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Informatik : Jurnal Ilmu Komputer Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) METIK JURNAL Scientific Journal of Informatics Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal PkM (Pengabdian kepada Masyarakat) Kresna: Jurnal Riset dan Pengabdian Masyarakat Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Algoritma Jurnal Ticom: Technology of Information and Communication Journal of Social And Economics Research Journal Of Communication Education Telematika MKOM Jurnal INFOTEL Jurnal Ticom: Technology of Information and Communication journal of social and economic research JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
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Journal : IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

Optimization of LZW Compression Algorithm With Modification of Dictionary Formation Restu Maulunida; Achmad Solichin
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 1 (2018): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.28707

Abstract

At present, the need to access the data have been transformed into digital data, and its use has been growing very rapidly. This transformation is due to the use of the Internet is growing very rapidly, and also the development of mobile devices are growing massively. People tend to store a lot of files in their storage and transfer files from one media to another media. When approaching the limit of storage media, the fewer files that can be stored. A compression technique is required to reduce the size of a file. The dictionary coding technique is one of the lossless compression techniques, LZW is an algorithm for applying coding dictionary compression techniques. In the LZW algorithm, the process of forming a dictionary uses a future based dictionary and encoding process using the Fixed Length Code. It allows the encoding process to produce a sequence that is still quite long. This study will modify the process of forming a dictionary and use Variable Length Code, to optimize the compression ratio. Based on the test using the data used in this study, the average compression ratio for LZW algorithm is 42,85%, and our proposed algorithm is 38,35%. It proves that the modification of the formation of the dictionary we proposed has not been able to improve the compression ratio of the LZW algorithm.
Optimization of Gradient Boosting Method for Predicting Narcissistic Personality Disorder (NPD) in Employees Solichin, Achmad; Pramudita, Bagas; Painem, Painem; Pradiptha, Anindya Putri
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 2 (2025): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.103551

Abstract

Narcissistic Personality Disorder (NPD) is a serious challenge in modern workplace environments; however, early detection and appropriate intervention remain unmet needs. This research aims to address the issue by proposing an intelligent system model based on machine learning, utilizing the Gradient Boosting method to predict NPD. The Gradient Boosting method was chosen for its ability to handle complex data and gradually improve prediction performance. This model is integrated with employee data, including a range of psychological, behavioral, and demographic variables relevant to NPD. The primary contribution of this research is the development of a predictive model that can assist organizations in identifying and providing early intervention to employees at risk of developing NPD. In doing so, it is expected to reduce the negative impact of NPD on the workplace, such as interpersonal conflicts and decreased productivity. The study shows significant results in the model's classification performance after applying Recursive Feature Elimination (RFE) to optimize the Gradient Boosting method. The accuracy rate reached 82%, an improvement from the previous 79% achieved using the Gradient Boosting Classifier. This indicates that the RFE-Gradient Boosting model has greater potential in classifying employees who genuinely have narcissistic personality disorder versus those who do not.
Co-Authors Abdullah 'Alim Achmad Maulana Agus Harjoko Agus Santoso Ahmad Ihsanudin Ahmad Zainul Mafakhir Akbar, Kafi Kurnia Alfredo Pasaribu Alhafiz, Muhammad Ihza Ananda Surya, Archie Andi Hakim Arif Andi Jumardi Anggi Ayu Ningtyas Anindya Putri Pradiptha Arif, Andi Hakim Arista Riski, Nanda Asmoro, Phaksi Bangun Bayu Raditya Nasution Bernadeta Asri Rejeki Tulodo Chandra, Joko Christian Dasril Aldo Dedy Mirwansyah Dewantara, Erno Kurniawan Dhiesky Chaerullah Dwi Kristanto Emil Salim Fadlan Amrullah Fahrullah Fahrullah Galih Gumilar Widhasmara Goenawan Brotosaputro Hanafi, Mohammad Afif Hari Soetanto Iqbal Chalid Irennada Ismail Adi Susanto Khaeri Diniari Khansa Khairunnisa Kurnianta, Kristana Lia Amellia Putri Lutfi Nukman Majid, Muhammad Farras Masdar Desiawan Mochammad Andika Putra Mohammad Syafrullah Muhamad Refaldi Muhammad Agus Arianto Muhammad Agus Arianto Muhammad Ali Akbar Muhammad Arif Kurniawan Muhammad Fahrizal Muhammad Hamdi Sukriyandi Muhammad Verdiansyah Muharam, Asep Budiyana Nariza Wanti Wulan Sari Nazori AZ Noor Ferdyansyah Nugroho, Ludi Obby Oktafianto Painem, Painem Painem, Painem Pradana, Rizky Pradiptha, Anindya Putri Pramudita, Bagas Prayogi, Muhamad Nur Rahmat Kurniawan Rasyid, Annisa Ratna Kusumawardani Reka Dwi Syaputra Restu Maulunida Reva Ragam Santika Richki Hardi Riki Wijaya Rizki Darmawan, Dika Robby Suganda Rusdah Rusdah Saddam, M Amiruddin Setiyadi, Prambudi Suherman Achmad Syahrul, Ahmad Tan Wee Chang Tetlageni, Muhamad Ridho Triyono, Gandung Ummu Habibah Romlah Utomo Budiyanto Wahyu Desena Wati, Lisna Wirasno, Wirasno Zainal A. Hasibuan Zulfikar Rosadi