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All Journal Jurnal Sistem Komputer & Kecerdasan Buatan JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknik Informatika (JUTIF) International Journal of Engineering, Science and Information Technology Jurnal Pengabdian kepada Masyarakat Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Abdi Jurnal Publikasi Power Elektronik : Jurnal Orang Elektro Digitus : Journal of Computer Science Applications Jurnal Ilmu Komputer
Anggai, Sajarwo
Universitas Pamulang
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Journal : Jurnal Ilmu Komputer

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Prediksi Inflow Daerah Aliran Sungai Larona Dengan Model Seasonal Autoregressive Integrated Moving Average Tukiyat, Tukiyat; Sutrisno, Sutrisno; Anggai, Sajarwo
Jurnal Ilmu Komputer Vol 1 No 2 (2023): Jurnal Ilmu Komputer (Edisi Desember 2023)
Publisher : Universitas Pamulang

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Larona Watershed (DAS) Inflow Prediction entering the reservoir has a very important role in managing the reservoir's water resources. Various approaches using mathematical models have been carried out, the results of which can be used as management tools to understand estimates and predictions of future inflow values, especially in the context of managing and planning water utilization for company needs at PT Vale Indonesia Tbk. The research aims to find a prediction model for the water inflow of the Towuti, Matano and Mahalona reservoirs. The research method uses a statistical approach using the SARIMA (Seasonal Autoregressive Integrated Moving Average) model. Research data, time series data, monthly inflow of the Larona watershed for January 2006 – December 2019. The research results showed that the best model was SARIMA (2,0,1)(0,1,1)12. The mathematical model prediction formulated is 4.786 + 1.459t-1 – 0.648t-2 – 0.714 e_(t-1). The model accuracy level was tested using the RMSE (Root Mean Squared Error) criteria of 0.767, MAE (Mean Absolute Error) level of 0.592, MAPE (Mean Absolute Percentage Error) of 14.58. To validate the predicted values, the F test, Siegel-Turkey, Bartlett, Levene was carried out at the α=5% level. The test results for the difference between actual and predicted values were concluded to accept the null hypothesis, which means that there is no significant difference between the actual data values and the predicted data values.
Analisis Minat Siswa Dalam Memilih Kompetensi Keahlian Dengan Metode Simple Additive Weighting (SAW) dan Multi-Objective Optimization on The Basis of Ratio Analysis (MOORA) Pahira, Wulan; Anggai, Sajarwo; T., Thoyyibah
Jurnal Ilmu Komputer Vol 1 No 2 (2023): Jurnal Ilmu Komputer (Edisi Desember 2023)
Publisher : Universitas Pamulang

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Every student who graduates from middle school plans to continue their education to the high school/vocational school level. Vocational Schools are schools that develop and prepare students to be able to work in their respective fields. There are 3 Skill Competencies, namely, 1) Institutional Financial Accounting, 2) Office Management and Business Services, and 3) Computer Network and Telecommunications Engineering. Of these 3 skill competencies, prospective students need to consider them in choosing skill competencies that suit their interests and talents, because after choosing skill competencies students will carry out education for approximately 3 years. In creating this skill competency selection system, the author used 2 methods, namely the SAW method and the MOORA method with 6 criteria: mathematics scores, ICT scores, English scores, math test scores, computer knowledge, and psychological tests. Based on the results of research using these two methods, the results obtained with preference values A1=0.782, A2=0.72, A3= 0.725, then the MOORA method obtained preference values A1=0.601, A2=0.53, and A3=0.527 with these two methods showing the same results as The highest preference value is A1. So that students enter the Institution's Financial Accounting Skills Competency. After testing the system using the SUS method, the percentage results were 75.9% (Acceptable) so that this system can be accepted and used effectively and efficiently in selecting skill competencies.
Pengembangan Prototipe Alat Sortir Berdasarkan Warna Berbasis Internet of Things (IoT) Menggunakan Thingspeak Imron, Ali; Anggai, Sajarwo
Jurnal Ilmu Komputer Vol 2 No 1 (2024): Jurnal Ilmu Komputer (Edisi Juli 2024)
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Manual sorting processes in the agriculture and manufacturing industries often require significant costs, time, and labor, impeding production efficiency and increasing the risk of errors. The proposed solution is to utilize an automated sorting device based on the color of the objects. This sorting prototype was developed using IoT technology with the ThingSpeak platform through a Research and Development method. The development steps included needs analysis, system design, prototype implementation, testing, and performance evaluation. The prototype incorporates a TCS 3200 color sensor, Nodemcu ESP8266 IoT module, servo motor, and the ThingSpeak platform. Testing was conducted using red, green, orange, and black objects. The test results demonstrated that the prototype achieved high accuracy in color identification, rapid data processing response, and consistent data transmission to ThingSpeak. The prototype was successfully developed according to the plan. Performance evaluation indicated its effectiveness in supporting color-based sorting processes in the agriculture and manufacturing industries. This prototype significantly contributes to the automation of industrial processes, particularly in using color as the primary criterion for object separation.
Analisis Prediksi Hasil Pemilu Legislatif DPR RI DKI Jakarta Tahun 2024 Menggunakan Metode Random Forest dan Gradient Boosting Effendy, Rangga Febrian; Susanto, Agung Budi; Anggai, Sajarwo
Jurnal Ilmu Komputer Vol 2 No 1 (2024): Jurnal Ilmu Komputer (Edisi Juli 2024)
Publisher : Universitas Pamulang

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In general elections, it is closely related to predictions, predictions play an important role in obtaining results in future legislative elections. Predicting general election results can be done through a series of processes to find patterns and knowledge from a set of data using data mining techniques. To get accurate prediction results in the future, a method is needed that can be used as predictive modeling. This research aims to find out the results of model testing and predictions for the 2024 DPR RI DKI Jakarta legislative election using random forest and gradient boosting methods and to find out patterns and knowledge from the prediction results themselves. Based on the model testing results, the gradient boosting method has an accuracy value of 95.8%, precision 72.2% and recall 61.9%. Meanwhile, random forest has an accuracy value of 95.4%, precision 63.6% and recall 33.3%. The pattern and knowledge from the prediction results is that the elected legislative candidates on average are in serial numbers 1 and 2, have valid votes starting from 63,529, are male and have a doctoral degree.
Ekstraksi Topik dalam Dataset Menggunakan Teknik Pemodelan Topik Anggai, Sajarwo; Tukiyat; Rivai, Abu Khalid; Zain, Rafi Mahmud
Jurnal Ilmu Komputer Vol 2 No 1 (2024): Jurnal Ilmu Komputer (Edisi Juli 2024)
Publisher : Universitas Pamulang

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The issue in this research is the lack of understanding regarding the main topics and their changes in speeches and media publications related to President Joko Widodo. This study aims to identify, analyze, and predict changes in key topics within speeches, statements, and media publications related to President Joko Widodo using Latent Dirichlet Allocation (LDA) topic modeling techniques. The research employs a quantitative approach to analyze President Joko Widodo's speech texts using the Latent Dirichlet Allocation (LDA) method. The process began with scraping documents from the official website of the Republic of Indonesia's Secretariat, resulting in 5,988 speech transcripts from October 20, 2014, to March 2, 2024. Text preprocessing involved tokenization, stopword removal, and stemming/ lemmatization, followed by dictionary-term formation. The findings indicate that the model with k=16 has the highest coherence (0.554) and the best perplexity at k=21 (-13.130). The main topics identified include Nationalism and National Values, Regional Government, and Education and Children. Topic visualization with PyLDAvis aids in the exploration and identification of topics, providing insights for decision-making and policy development. To enhance understanding of topic changes, it is recommended to conduct trend analysis on key topics over time. This will help identify how President Joko Widodo's priorities shift and respond to new issues. By monitoring these trends, the research can provide deeper insights into the evolution of policies and the President's focus.
Pengembangan Sistem Employee Self Service (ESS) Berbasis Web Terintegrasi Dengan Kinerja Karyawan (Studi Kasus: Astrido Group) Wibowo, Satria Ardi; Susanto, Agung Budi; Anggai, Sajarwo
Jurnal Ilmu Komputer Vol 2 No 1 (2024): Jurnal Ilmu Komputer (Edisi Juli 2024)
Publisher : Universitas Pamulang

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Technological developments in the 4.0 era require humans to act effectively and efficiently. Astrido Group is a company operating in the automotive sector, especially car sales and services. The expected goal of this research is to build an information sistem that makes it easier for employees to update personal data, download attendance reports, process leave applications and approvals and obtain employee performance information. The sistem development method is Rapid Application Development (RAD) and modeled using the Unified Modeling Language (UML). Focus Group Discussion (FGD) Used as validation testing. The resulting software quality test is based on the four software quality characteristics of the ISO 9126 model, namely: functionality, reliability, usability and efficiency which are combined using the questionnaire method. The Black Box test results were 100%, which indicates the system was well received by users, while testing with Acunetix WVS was at Threat Level 2, which indicates the application being built is quite safe.
Analisis Disaster Recovery Plan Keamanan Data dan Informasi Menggunakan NIST Framework (Studi Kasus: Biro Teknologi Informasi Yayasan Pendidikan Internal Audit) Muhamad, Faruk; Tukiyat; Anggai, Sajarwo
Jurnal Ilmu Komputer Vol 2 No 1 (2024): Jurnal Ilmu Komputer (Edisi Juli 2024)
Publisher : Universitas Pamulang

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Disasters are unexpected and potentially significant risks to the continuity of company and organization operations, especially those related to information systems and information technology (IS/IT). The Internal Audit Education Foundation (YPIA) in handling disasters related to data and information security often faces obstacles that cause problems that become more widespread in the future. Therefore, a disaster recovery plan (DRP) becomes an urgent need. The purpose of this study is to evaluate resilience to disasters and data and information security attacks, and to ensure better business continuity in the face of emergency situations. Researchers use the National Institute of Standards and Technology (NIST) Framework in conducting a DRP analysis of security and data. The study begins by identifying and evaluating risks, conducting risk assessments, conducting Business Impact Analysis (BIA) determining preventive controls, and formulating contingency strategies. This study produces priority handling of high maturity risks in data damage, with an initial risk value of 3.8 and an impact of 4.4. After the control was carried out, there was a residual risk with a risk value of 1.6 and an impact of 3, with a very low maturity level and a residual value of 13.5 (80%). The reduction in the risk of data damage was significant with a very low residual value, indicating that the implementation of DRP using the NIST Framework in risk mitigation on critical assets of the Internal Audit Education Foundation was quite effective.
Perbandingan Efisiensi Metode Simple Additive Weighting (SAW) dan Weighted Product (WP) Dalam Sistem Penilaian Kinerja Guru Honorer Maulana, Romdon; Handayani, Murni; Anggai, Sajarwo
Jurnal Ilmu Komputer Vol 2 No 1 (2024): Jurnal Ilmu Komputer (Edisi Juli 2024)
Publisher : Universitas Pamulang

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The teacher is the key to the success of the quality of education in this country with the main task of educating, teaching, guiding, directing, training, assessing, and evaluating students in early childhood education through formal education, basic education and secondary education. Teachers with honorary teacher status are the beginning of a career path for a teacher. assessment of teacher performance as an illustration of the results of the performance and teaching ability of a teacher. In addition to encouraging motivation, dedication, loyalty, professionalism, and improving the quality of education, teacher performance assessments are also used as a reference, and recommendation for raising the teacher's career path. The methods used in teacher performance assessment research or with similar objects are the SAW method, WP method, Fuzzy Logic, Analytical Hierarchy Process (AHP) method, Decision Tree method, Composite Performance Index (CPI) method and TOPSIS method. However, there has been no research comparing the SAW method with the WP method in terms of the efficiency of the calculation process time, so it is not yet known which method is more efficient in terms of calculation process time in the teacher performance appraisal system, so in this research, we will compare the SAW method with the WP method in honorary teacher performance assessment system, so that it can be seen which method is more efficient in terms of the calculation process for honorary teacher performance assessment.
Analisis Data Produksi Biskuit Dengan Algoritma Naive Bayes Dan Random Forest Sabarrudin; Agung Budi Susanto; Sajarwo Anggai
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

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In the manufacturing industry, production problems often occur, often production does not match market demand, production is not well planned, therefore this study aims to develop a classification model using machine learning based on the Naive Bayes and Random Forest algorithms to classify biscuit production data. The main focus of this study is to utilize variables such as dough, number of mixers, production time parameters, and other relevant production factors to improve accuracy in classification. The dataset used in this study includes information from several previous production periods, namely data in 2019-2023, which is then used to train and test the Naive Bayes and Random Forest algorithm models. The training and validation process is carried out using commonly used model performance evaluation techniques. The results of the study show that the Random Forest model is able to provide high accuracy, namely 97.54% while Naive Bayes is 96.45%. Further analysis was also carried out to identify the variables that most influence production results, providing additional insights for optimizing the production process. The results of this study can contribute to the development of classification models for the food and beverage industry, especially in biscuit products, but also offer a more specific view of the factors that influence biscuit production. The implementation of this study can be a basis for manufacturers to make more precise and effective decisions in managing their production.
Analisis Sentimen Opini Masyarakat Terhadap Pemilu 2024 Melalui Media Sosial X Dengan Menggunakan Naive Bayes, K-Nearest Neighbor Dan Decision Tree Cut Shifa Khoirunnisa; Tukiyat; Sajarwo Anggai
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

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This study aims to analyze public opinion sentiment towards the 2024 election using three machine learning classification algorithms: Naïve Bayes, K-Nearest Neighbors and Decision Tree. The data used in this study were taken from Social Media X, which is one of the social media platforms with a large and diverse data volume. The object of this study is public opinion expressed on Social Media X, with the subject of research in the form of tweets taken using the Twitter API, resulting in 5000 data with 2469 clean data. Data analysis involves text extraction and preprocessing processes that include data cleaning, tokenization, stopwords and stemming. The results of the study show the distribution of sentiment as follows: positive sentiment dominates with 96% of the total tweets, followed by neutral sentiment at 2% and negative sentiment at 1%. From the modeling results among the algorithms tested, K-Nearest Neighbors showed the best performance with an accuracy value reaching 97.50%, followed by Decision Tree having a performance with an accuracy value of 97.25% while Naïve Bayes had the lowest performance with an accuracy value of 96.14%. Although there is variation in performance among the algorithms used, none of them are completely consistent in classifying sentiment. This study makes a significant contribution in mapping public sentiment related to the 2024 election in Indonesia through data analysis from social media X, and provides insight into the effectiveness of various Data Mining Algorithms in sentiment analysis.
Co-Authors Abu Khalid Rivai Agnia Bilqisti AGUNG BUDI SUSANTO Agung Budi Susanto Ali Imron Arya Adhyaksa Waskita Brando, Charlo Chaerul Umam Cut Shifa Khoirunnisa Dhaman, Dhaman Effendy, Rangga Febrian Gumay, Rizki Izandi Hanto Sujatmiko Junedi, Beni Maulana, Romdon Muhamad, Faruk Muhammad Nana Muhammad Rafif Misbahuddin Murni Handayani Pahira, Wulan Rafi Mahmud Zain Sabari Sabari Sabarrudin Slamet Wiyono Sugiyo Supriatna, Dahlan Sutrisno, Sutrisno T., Thoyyibah Taufiqur Rahman, Taufiqur Tukiyat Tukiyat Tukiyat, Tukiyat Waskita, Arya Adhyaksa Wibowo, Rizki Septiyanto Wibowo, Satria Ardi Wily, Wily Arisandi Winarni Zain, Rafi Mahmud