Article Per Year (5 Year)
p-Index From 2021 - 2026
7.256
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Sistemasi: Jurnal Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta CogITo Smart Journal JTERA (Jurnal Teknologi Rekayasa) Jurnal Sisfokom (Sistem Informasi dan Komputer) KACANEGARA Jurnal Pengabdian pada Masyarakat JSiI (Jurnal Sistem Informasi) JURTEKSI CCIT (Creative Communication and Innovative Technology) Journal Jurnal Pendidikan dan Konseling Jurnal Investasi Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Madaniya Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika Sekretari & Administrasi (Serasi) Jurnal Teknik Informatika (JUTIF) Teknika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia KLIK: Kajian Ilmiah Informatika dan Komputer Decode: Jurnal Pendidikan Teknologi Informasi Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Algoritma Jurnal Ticom: Technology of Information and Communication Telematika MKOM AMMA : Jurnal Pengabdian Masyarakat The Indonesian Journal of Computer Science Jurnal Pengabdian Masyarakat Tekno INOVTEK Polbeng - Seri Informatika Jurnal Pintar Pengabdian dan Pemberdayaan Masyarakat
Triyono, Gandung
Universitas Budi Luhur
Religion Agriculture, Biological Sciences & Forestry Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Social Sciences Other

Published : 85 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search
Journal : Jurnal Teknik Informatika (JUTIF)

 1 
DECISION SUPPORT SYSTEM FOR PREDICTING EMPLOYEE LEAVE USING THE LIGHT GRADIENT BOOSTING MACHINE (LIGHTGBM) AND K-MEANS ALGORITHM Vasthu Imaniar Ivanoti; Megananda Hervita P.; Gandung Triyono; Dyah Puji Utami
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.3.1084

Abstract

Nowadays, decision support systems have gained wide popularity not only in private companies but also in government sectors. These systems play a crucial role in assisting leaders during the decision-making process. The effective functioning of the government heavily relies on employee performance, which requires discipline in carrying out their duties and responsibilities. Employee discipline is closely linked to their attendance, including leave-taking. Therefore, analyzing employee leave data can reveal trends and interrelationships, providing leaders with valuable information and insights for determining employee leave policies. To address this issue, data mining applications such as the Light Gradient Boosting Machine (LightGBM) regression prediction model can be utilized. This model takes into account factors like gender, age, and the starting year of leave to predict the number of employees who take annual leave simultaneously with holidays. Additionally, clustering algorithms like K-Means can be employed to group reasons for leave into clusters, identifying common leave patterns among employees. In this study, employee leave application data from January 2018 to July 2022 was collected from the Leave module within the HRIS (Human Resource Information System) application. The research outcomes encompass a dashboard visualization presenting descriptive analysis and modeling using LightGBM. The modeling results yielded reasonably accurate predictions, as evidenced by model testing that showed a difference of only 1 employee. Additionally, K-Means clustering formed 4 clusters of leave reasons, with the majority being family-related, illness, childcare, and elderly care. The dashboard can be used by management as a consideration for approving employee leaves, ensuring well-planned leave scheduling for the following year and minimizing disruption to work execution in each department.
DEVELOPMENT OF A STOCK PURCHASE RECOMMENDATION SYSTEM APPLICATION Maruanaya, Greghar Juan Tjether; Triyono, Gandung; Maruanaya, Rita Fransina
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.2024.5.4.2322

Abstract

Investing in stocks has become a significant source of passive income through indirect earnings with minimal activity. Choosing stocks for investment requires careful analysis. The Indonesia Stock Exchange has 866 listed stocks, divided into several indices, including IDXBUMN20, which includes 20 stocks from state-owned enterprises (BUMN), regional-owned enterprises (BUMD), and their affiliates. This index helps traders monitor the performance of BUMN stocks. The list of IDXBUMN20 stocks includes ADHI, ANTM, BBNI, AGRO, BBRI, BRIS, BBTN, BJBR, BMRI, MTEL, ELSA, JSMR, PGAS, PTBA, PTPP, SMGR, TINS, TLKM, WIKA, and WSKT. Traders need recommendations to select stocks with positive trends. Forecast analysis becomes a potential solution to provide references for stocks with positive trends. This study applies the Simple Moving Average (SMA) method to forecast the prices of IDXBUMN20 stocks. The SMA will be measured using 30, 40, 50, and 60-day periods as indicators. This method is chosen for its ability to identify stock price trends by calculating the average closing price over a specific period. Therefore, forecasting results using SMA will provide a more accurate picture of stock price movements and aid in making better investment decisions. From the forecasting results using the SMA method, recommendations for the top five stocks showing positive trends will be obtained. Subsequently, to determine which stock is most recommended, a stock recommendation model will be developed using the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. TOPSIS will consider various criteria such as average frequency, Price Earning Ratio (PER), Price Book Value (PBV), Return on Assets (ROA), and Return on Equity (ROE). The results showed that the most recommendable stocks based on the positive trend of price movements are SMGR for indicator 30 and TLKM for indicators 40, 50 and 60. Therefore, it can be concluded that the most recommended stock is TLKM (PT Telkom Indonesia (Persero) Tbk).This recommendation model is expected to help traders select the stocks with the best investment potential, maximizing profits and minimizing investment risks in the capital market.
SENTIMENT ANALYSIS AND ENTITY DETECTION ON NEWS HEADLINES TO SUPPORT INVESTMENT DECISIONS Adhi, Ajar Parama; Umuri, Khairil; Triyono, Gandung
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.3434

Abstract

Accurate investment decisions are often influenced by information available in the media. News headlines, as part of information media, can provide an initial picture of market sentiment and ongoing trends. This research examines the importance of making appropriate investment decisions with a focus on sentiment analysis and entity detection in news headlines as supporting tools. Through machine learning-based sentiment analysis and Named Entity Recognition (NER) techniques, this study identifies opinions and entities such as company names, stock indices, and industry sectors in news headlines. This research compares three machine learning algorithms, namely SVM, Naive Bayes, and Random Forest using cross-validation. The result shows that the best algorithm is SVM with weighted average F1-score of 76,68%. Furthermore, hyperparameter optimization is performed using Optuna for the SVM algorithm, which is an innovation in the context of sentiment analysis on news headlines in Indonesia. The result shows an increase in weighted average F1-score to 78,14%. For NER, a rule-based method is used by utilizing the Jaro-Winkler string similarity function. The combination of sentiment analysis and NER is then presented in the form of a dashboard using Google Looker Studio tools, with data from sentiment analysis and NER results being processed periodically and automatically using Google Workflows. This research makes a significant contribution by expanding the scope of analysis from just one or a few issuers to all entities published on news portals thanks to NER support, making the results relevant to support investment decisions that are responsive to dynamic market changes.
COMPARISON OF SAW AND TOPSIS METHODS TO DETERMINE THE BEST SERVICE DESK AGENT Suryani; Prasetyo, Angger Totik; Triyono, Gandung
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1675

Abstract

Pusintek's Service Desk, as a single point of contact, has quite high work demands with many tasks and requests handled. In order to improve the performance of Service Desk agents, the organization can give awards to the best Service Desk agents. However, there are obstacles in selecting the best Service Desk agent because there is still a subjective element in the assessment of Service Desk agents. So that a decision support system is needed that is in accordance with the weight of the organization's assessment criteria. This research proposes an approach in selecting the best Service Desk agent using the Simple Additive Weighting (SAW) method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in processing and ranking agent value data. This research focuses on assessing agents based on key parameters, namely ticket processing time (service response time), agent attendance data, assignment weight and assessment from other coworkers. The number of agents assessed was seventeen. The results of this study obtained the highest value using the SAW method of 2.22 for A1, while the calculation using the TOPSIS method, the highest value on A1 is 0.74 and the accuracy rate using the SAW method is 82.35% while the TOPSIS accuracy is 41.18%..
APPLICATION OF ENSEMBLE METHOD FOR EMPLOYEE TURNOVER PREDICTIONS IN FINANCIAL SERVICES COMPANY Fadel, Muhamad; Kanasfi, Kanasfi; Arifin, Zainal; Triyono, Gandung
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.1871

Abstract

High employee turnover is a challenge for every company, considering that employees are a valuable asset for the company. A high employee turnover rate indicates the high frequency of employees leaving a company. This will harm the company in terms of time, costs, human resources, and reduce the company's reputation. Low employee turnover is an objective for every company in its efforts to achieve its vision and mission, the employee turnover rate is high at 78.97% at PT. HCI operating in the financial services sector can have a negative impact on the company's reputation. Therefore, there is a need to analyze and predict employee turnover so that company management can take preventive and persuasive actions so as to reduce employee turnover rates. Therefore, a tool is needed to predict whether an employee will leave the company. This paper aims to predict the possibility of employees out of the company using the ensemble method, which is a method that uses a combination of several algorithms consisting of base learners and individual learners, algorithms with the ensemble method used are stacking, random forest, and adaboost, then comparing the result to get the best accuracy. The test results prove that the Stacking algorithm technique is the best model with the highest score in terms of accuracy with a value of 86.84%, while the Random Forest and AdaBoost algorithm techniques have a value of 81.04% and 80.30%. With this high accuracy value, the Stacking model is proven to have better individual performance in analyzing employee turnover predictions in human resource applications in companies.
Co-Authors - Sumardianto Abdul Hamid Abdurrahman, Faris Nur Achmad Ardiansyah Achmad Solichin Achmad Syarif Adhi, Ajar Parama Aditya Ikhbal Maulana Agus Umar Hamdani Aji Guntoro Al Ghozali, Isnen Hadi Al-akbari, Munawir Fikri Ananda Dian Nugraha Angga Prasetyo Anggita Pamukti Anggraini Ujianti Anwarsyah, Anwarsyah Aris Subagyo, Wismoyo Asep Lukman Arip Hidayat Assegaf , Noval Azizi, Hibatul Chaerul, Muh Coudry Bernadeth Dana Indra Sensuse Daniel Iskandar Dede Wahyu Saputra Dermawan Ginting Devy Fatmawati Dini Astuti Dini Handayani, Dini Djafar, Muhammad Agung A. Djati Kusdiarto Dolly Virgian Shaka Yudha Sakti Dwi Kristanto Dyah Puji Utami Effendi , Muhtar Eliyani, Eliyani Ery Rinaldi Fachrurozy, Achmad Fadel, Muhamad Fahlevi, Noval Fajriah, Riri Febri Maulana Febrianti, Rizkia Saski Feby Lukito Wibowo Firmansyah, Maulana Gilang Ramadhan Hadi rahadian Hafiz, Rahmad Hakim, Sulaiman Hanifa, Annisa Hardjianto, Mardi Helmi Zulqan Hendra Adi Saputra Henny Idam Risnaputra Iman Permana, Iman Indra Indra Jotri Firdani Maharaja Juhari Juhari, Juhari Jumaryadi, Yuwan Kanasfi, Kanasfi Kiki Ari Suwandi kosasih Lestari, Triardani Lis Suryadi Lis Suryadi, Lis Lutfan Lazuardi Luthfi Mawardi Mahendra, M. Azmi Malik Aziz Habibie Maruanaya, Greghar Juan Tjether Maruanaya, Rita Fransina Maskur A, Moch Riyadi Masnuryatie, Masnuryatie Maya Asmita Megananda Hervita P. Melyana, Melyana Mepa Kurniasih MHD. Reza M.I. Pulungan Moch. Rezaf Ivanka Haris Mohammad Aldinugroho Abdullah Muhamad Dikhi Rohman Munandar, Muhamad Arief Muttaqin, Zaenul Ningrum, Sekar Ayu Nurhikmah, Suci Oktiara, Dara Putri Pebry, Fachry Ajiyanda Pirman, Arif Prasetia, Andika Rohman Prasetyo, Angger Totik Rahmat Hidayat Ramadani, Romi Reza Ariftiarno Ridho Firmansyah Ridho Putra Kusmanda Riki Ramdani Saputra Rima Tamara Aldisa Rinto Prasetyo Adi Rizka Pitriyani Rizky Adhi Saputra Rizky Fernanda Aprianto Rizky Tahara Shita Rojakul, Rojakul Rudi Hartono Rudi Hidayat Ryan Prasetya Safrina Amini Septiadi, Septiadi Setyadin, Rahmat Dipo Sittah Ifadah Sri Hartati Sri Melati Subekti, Yogi Agung Sudiyatno Yudi Nugroho Sufyan Asaury, Akhmad Suriah Setiana Widiastuti SURYANI Syarif Hidayatulloh Tansya Ingmukti Taryono, Ono Tunggal Saputra, Tri Aji Umar Alfaruq Umuri, Khairil Utomo Budiyanto Vasthu Imaniar Ivanoti Wahyu Adi Setyo Wibowo Wahyu Cesar, Wahyu Wahyuningram, Nugroho Warih Dwi Cahyo Wawan Gunawan Widyanto, Tetrian Wilsen Grivin Mokodaser Winasis, Reza Handaru Wisanto, Aditya Agus Wisnu Cahyadi Wulan Trisnawati Yasmin , Nadia Yeros Fathullah Achmad Zainal Arifin