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J-SAKTI (Jurnal Sains Komputer dan Informatika)
Published by Sekolah Tinggi Ilmu Komputer Tunas Bangsa
ISSN : 25489771     EISSN : 25497200     DOI : http://dx.doi.org/10.30645/j-sakti
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy
J-SAKTI adalah jurnal yang diterbitkan oleh LPPM STIKOM Tunas Bangsa yang berfokus di bidang Manajemen Informatika. Pengiriman artikel tidak dipungut biaya, kemudian artikel yang diterima akan diterbitkan secara online dan dapat diakses secara gratis. Topik dari J-SAKTI adalah sebagai berikut (namun tidak terbatas pada topik berikut) : Artificial Intelegence, Digital Signal Processing, Human Computer Interaction, IT Governance, Networking Technology, Optical Communication Technology, New Media Technology, Information Search Engine, Multimedia, Computer Vision, Information System, Business Intelligence, Information Retrieval, Intelligent System, Distributed Computing System, Mobile Processing, Computer Network Security, Natural Language Processing, Business Process, Cognitive Systems, Software Engineering, Programming Methodology and Paradigm, Data Engineering, Information Management, Knowledge Based Management System, Game Technology.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 499 Documents
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Clustering Koridor Transjakarta Berdasarkan Jumlah Penumpang Dengan Algoritma K-Means Supriyatna, Adi; Carolina, Irmawati; Janti, Suhar; Haidir, Ali
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 4, No 2 (2020): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (978.801 KB) | DOI: 10.30645/j-sakti.v4i2.259

Abstract

Transportation is one of the facilities that make it easy for humans to carry out activities to move places using vehicles that are driven by humans or machines. Based on data obtained from data.jakarta.go.id, the number of Transjakarta bus passengers in corridors 1 to 13 of 2017 amounted to 114,239,960, and in 2018 there were 121,918,964 passengers. The algorithm used in this research is K-Means Cluster, which is implemented using Microsoft Excel and Rapidminer Studio. The purpose of this study is to cluster Transjakarta corridors based on the number of passengers divided into 3 clusters: high, medium, and low. The results of data processing show that the Transjakarta corridor data cluster is based on the number of passengers using the K-Means cluster algorithm using Microsoft Excel and Rapidminer Studio to produce 3 clusters, namely cluster 1 with the highest number of passengers, one corridor, cluster 2 with the number of passengers being nine corridors and cluster 3 or 0 with a low number of passengers there are three corridors. The highest number of passengers is corridor one which serves the Blok M - Kota route, indicating that the Blok M - Kota route is the most used by Transjakarta passengers.
Analisis Sentiment Masyarakat terhadap Kasus Covid-19 pada Media Sosial Youtube dengan Metode Naive bayes Ahmadi, Muhammad Iqbal; Gustian, Dudih; Sembiring, Falentino
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1114.196 KB) | DOI: 10.30645/j-sakti.v5i2.378

Abstract

The development of Covid-19 cases in Indonesia continues to increase. with the continued increase in these cases causing panic among the public regarding the presence or absence of this corona virus, in the midst of this condition an effective and efficient communication pattern is needed in providing education and information about this corona virus, for example with social media Youtube. Many people's responses to this news are expressed in the comments column. Therefore, a sentiment analysis model is needed to classify public comments into Positive, Negative and neutral. In this study, the Naive bayes method is used to build a sentiment analysis model for public responses about the news on the development of the Covid-19 case on the Youtube page, precisely on the KompasTV Chanel. Accuracy is 74% with the number of comments Positive 361, Negative 800 and neutral 490.
Prediksi Mahasiswa Berpotensi Berhenti Kuliah Secara Sepihak Menggunakan Data Mining Algoritma C4.5 Sudarsono, Bernadus Gunawan; Bani, Alexius Ulan
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 4, No 2 (2020): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1240.79 KB) | DOI: 10.30645/j-sakti.v4i2.227

Abstract

Students are a call to a student who has sat in college; there are many reasons why students decide to stop taking one-sidedly lectures which result in many losses that will be experienced by the campus, starting from reduced campus quantities and campus quality, adding to the accumulation of unclear student data and will cause and slow down the campus reporting performance. Hence, it is necessary to predict students who have the potential to stop studying unilaterally by looking at several criteria and digging up information on potential student data by applying data mining science and approaching one of the classification approach algorithms, namely the C4.5 algorithm; the results obtained from the study can see the predictive rule of students who have the potential to unilaterally quit college.
Prediksi Kepribadian Berdasarkan Media Sosial Twitter Menggunakan Metode Naïve Bayes Classifier A, Muhammad Ichsanudin; Irawan, Agung Susilo Yuda; solehudin, Arip
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (747.752 KB) | DOI: 10.30645/j-sakti.v5i2.394

Abstract

Analysis of a person's personality is very helpful as an assessment in various matters such as recruitment, career, health. The methods commonly used in personality analysis are interviews, observations, and questionnaire surveys. This study tries to provide a solution by simply using social media, namely twitter, by analyzing twitter user information data called tweets, this is to add to the method of personality analysis. The method used in this personality prediction research is to classify a tweet into 5 personality forms. The personality method used by the researcher is the Big Five Personality which consists of openness, conscientiousness, extraversion, agreeableness, and neuroticism with classification calculations using Naive Bayes. The result of this research is an accuracy of 42% with the highest class, namely Agreeableness.
Three Pass Protocol untuk Keamanan Kunci Berbasis Base64 pada XOR Cipher Sulaiman, Oris Krianto; Nasution, Khairuddin; Siambaton, Mhd. Zulfansyuri
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 4, No 2 (2020): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (781.259 KB) | DOI: 10.30645/j-sakti.v4i2.338

Abstract

XOR cipher is a message randomization algorithm by performing XOR logical operations for plaintext and key so that it becomes a ciphertext. The problem lies in the predictable use of XOR. Therefore, it uses key security using the Three Pass Protocol. This protocol secures communication for each party. The key used for Three Pass Protocol communication is Base64. The Three Pass Protocol scheme for XOR Cipher key security using Base64 has a weakness because it is due to the Base64 encoding process which by default is easy for others to know. So that this research can be developed again to replace Base64 into a better algorithm in terms of security.
Implementasi GPS (Global Positioning System) Pada Presensi Berbasis Android DI BMT Insan Mandiri Khoir, Syaiful Amrial; Yudhana, Anton; S, Sunardi
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 4, No 1 (2020): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (608.395 KB) | DOI: 10.30645/j-sakti.v4i1.182

Abstract

Attendance is a document that represents each employee at the company or representative. Some of the challenges experienced by KSPPS BMT INSAN MANDIRI are marketing that is difficult to carry out attendance when coming out of the office because of increased marketing activities that are directly related to members / out-of-office expenses. Therefore the researcher got findings to solve the problem that occurred by making an Android-based online attendance application that can be connected directly with the sarver provided by the office. This application is equipped with the introduction of security and the use of manipulations made by employees, and this application is equipped with monitoring features so that marketing managers and managers can set marketing positions in realtime. In this study, researchers used HTML and PHP programming languages for web applications that use presence servers / data centers, webside uses Google map APIs to obtain marketing positions, and presence reports can be purchased from the webside. The hope of this research is that the online android mobile attendance application with face recognition security can run well and meet the needs of the KSPPS BMT INSAN MANDIRI.
Perbandingan Algoritma ELM Dan Backpropagation Terhadap Prestasi Akademik Mahasiswa Pratiwi, Heny; Harianto, Kusno
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 3, No 2 (2019): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (551.042 KB) | DOI: 10.30645/j-sakti.v3i2.147

Abstract

Extreme Learning Machine and Backpropagation Algorithms are used in this study to find out which algorithm is most suitable for knowing student academic achievement. The data about students are explored to get a pattern so that the characteristics of new students can be known every year. The evaluation process of this study uses confusion matrix for the introduction of correctly recognized data and unknown data. Comparison of this algorithm uses student data at the beginning of the lecture as early detection of students who have problems with academics to be anticipated. The variables used are the value of the entrance examination for new students, the first grade IP value, Gender, and Working Status, while the output variable is the quality value as a classification of academic performance. The results of this study state that the Extreme Learning Machine algorithm has a 14.84% error rate lower than Backpropagation 28.20%. From the model testing stage, the most accurate result is the Extreme Learning Machine algorithm because it has the highest accuracy and the lowest error rate.
Penerapan Extreme Programming dalam Pengembangan Sistem Informasi Manajemen Pelayanan Publik Nurkholis, Andi; Susanto, Erliyan Redy; Wijaya, Suhenda
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (997.744 KB) | DOI: 10.30645/j-sakti.v5i1.304

Abstract

Sukarame District serves public administration activities such as information on population data and mail administration services. The conventional process currently running in Sukarame District only uses Microsoft Office for data storage so that it cannot be accessed by people in real-time. As a solution to the problem, we need a system that can help public service management to manage population data, public complaints, and service letters such as environmental permits, electricity subsidy permits, and business permits. This system was developed with the extreme programming development method with system design using a unified modeling language, namely use case diagram, class diagram, and activity diagram. To assess the feasibility of the system, testing was carried out using the black-box method which resulted in an accuracy of 100%, so that this system was fit for use functionally.
Analisis Kesiapan Blended Learning Di Lingkungan Program Studi Teknik Informatika Universitas PGRI Madiun Riyanto, Slamet; Mumtahana, Hani Atun
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 2, No 2 (2018): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (672.682 KB) | DOI: 10.30645/j-sakti.v2i2.82

Abstract

The purpose of this study was to determine the level of preparedness of blended learning in the Informatics Engineering Study Program Environment at the PGRI Madiun University. This study uses a survey research approach with a total sample of 195 people. The research instrument used questionnaires with Likert scale. Data analysis in this study uses descriptive analysis with learning readiness approach adopted from Aydin and Tasci. The result of the research shows that the readiness of blended learning in informatics engineering study program of PGRI Madiun University, for personal factor or dimension is assessed Not ready needs some works. Self-development factors or dimensions are assessed Not ready needs some works. Factors or dimensions of technology are assessed Ready but needs a few improvement. Factors or dimensions of innovation are rated Not ready needs some works.
Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter Normawati, Dwi; Prayogi, Surya Allit
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1308.118 KB) | DOI: 10.30645/j-sakti.v5i2.369

Abstract

Twitter is one of the social media that is currently in great demand by internet users. The number of tweets circulating on Twitter is not yet known whether these tweets contain more positive, negative, and neutral opinions. For that we need a system that can process data by applying sentiment analysis. This study uses the Naïve Bayes Classifier (NBC) method to analyze the level of sentiment towards data carried out by crawling on Twitter. The data studied as a simple case study uses only 8 tweet data which is divided into 5 training data and 3 test data. The data is processed using the preprocessing stage, then classified using the NBC method, the calculation of performance uses confusion matrix techniques. This study resulted in a structured exposure to the process and results of NBC implementation and performance testing using the confusion matrix which obtained 82% accuracy, 93% precision, and 52% recall. However, these results are more focused on ease explaining for each stage and process in more detail, not on the numbers obtained. Research with larger data will be carried out later by developing a computer-based application system.

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