Articles
<![CDATA[On the Periodical Nature of Annual Variation of the Regressions of the Martian Polar Caps Using the Phase Dispersion Minimization-(PDM) Method ]]>
<![CDATA[Iratius Radiman]]>;
<![CDATA[Chatief Kunjaya]]>;
<![CDATA[Andreas Widjaja]]>
<![CDATA[ Jurnal Matematika & Sains Vol 3, No 2 (1998) ]]>
Publisher : <![CDATA[Institut Teknologi Bandung ]]>
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<![CDATA[Data observations of the regressions of the Martian Polar Caps from 1905 to 1988 were reinvestigated through the Stellingwerfâs Phase Dispersion Minimization-(PDM) method. The results indicate that the annual variations of the regressions of the Martian Polar Caps is seen to have a periodical nature. The main period is found to be approximately 40 years while other periodical components of shorter duration seen in the data also exist. The shorter periods are 3, 6 and 8 years respectively. The 3 years period may be attributed to the systematic effect of 2-3 years interval in which the data was acquired. The 6 years may be a subharmonic component of the 3 years period, though it is blended. The 8 years period cannot be attributed to the same effects of observations, such as the cyclic appearances of each polar caps in the Martian epoch of observations. Irregularity of the epoch interval of observations prevent such a systematic effect to be seen. Therefore, we like to point out that the 8-years period is a real physical phenomenon of the annual variations of the polar regressions. This strongly support the findings of Iwasaki and Ebisawa on sizes of the South Polar Cap. The PDM-method is sufficiently general to analyze small sets of data involving missing observations and non-sinusoidal time variations. The method was used to ascertain results obtained previously through time-series calculations which involved missing observations. Discussions on probable relations between changes in solar radiation which might affect the annual regressions of the polar caps are also examined. ]]>
<![CDATA[Evaluasi Metodologi CI/CD untuk Pengembangan Perangkat Lunak dalam Perkuliahan ]]>
<![CDATA[Hapnes Toba]]>;
<![CDATA[Tjatur Kandaga Gautama]]>;
<![CDATA[Julio Narabel]]>;
<![CDATA[Andreas Widjaja]]>;
<![CDATA[Sendy Ferdian Sujadi]]>
<![CDATA[ JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 8, No 2 (2022): Volume 8 No 2 ]]>
Publisher : <![CDATA[Program Studi Informatika ]]>
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DOI: 10.26418/jp.v8i2.51992
<![CDATA[Saat ini sistem Continuous Integration (CI)/ Continuous Delivery (CD) merupakan standar baru dalam pengembangan perangkat lunak di industri. Sistem CI/CD merupakan langkah otomatisasi dari sebagian proses dalam pengembangan perangkat lunak. Ketika suatu sistem CI/CD digunakan oleh tim pengembang perangkat lunak maka akan diperoleh banyak data pemrosesan dan data hasil akhir dari proses CI/CD tersebut. Penelitian ini berupaya untuk mengevaluasi data yang terhimpun dalam sebuah sistem CI/CD dan diharapkan akan menemukan informasi yang bermanfaat sebagai umpan balik terhadap potensi sistem CI/CD dalam perkuliahan. Evaluasi riset dilakukan dengan metode survei pada kelas pilihan di semester ganjil tahun akademik 2021/22. Survei dimulai sejak masa ujian tengah semester sampai dengan akhir semester, yaitu pada saat mahasiswa peserta kelas mulai membuat sistem/ aplikasi guna memenuhi kelengkapan tugas besar mata kuliah. Adapun kelas yang dipilih tersebut adalah mata kuliah rekayasa perangkat lunak di program studi S-1 Teknik Informatika. Hasil survei menunjukkan bahwa mayoritas mahasiswa sangat antusias dan merasa penting untuk mendalami konsep CI/CD sebagai salah satu metode mutakhir pengembangan perangkat lunak.]]>
<![CDATA[Building Acoustic and Language Model for Continuous Speech Recognition in Bahasa Indonesia ]]>
<![CDATA[Vincent Elbert Budiman]]>;
<![CDATA[Andreas Widjaja]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v6i2.2684
<![CDATA[Here a development of an Acoustic and Language Model is presented. Low Word Error Rate is an early good sign of a good Language and Acoustic Model. Although there are still parameters other than Words Error Rate, our work focused on building Bahasa Indonesia with approximately 2000 common words and achieved the minimum threshold of 25% Word Error Rate. There were several experiments consist of different cases, training data, and testing data with Word Error Rate and Testing Ratio as the main comparison. The language and acoustic model were built using Sphinx4 from Carnegie Mellon University using Hidden Markov Model for the acoustic model and ARPA Model for the language model. The models configurations, which are Beam Width and Force Alignment, directly correlates with Word Error Rate. The configurations were set to 1e-80 for Beam Width and 1e-60 for Force Alignment to prevent underfitting or overfitting of the acoustic model. The goals of this research are to build continuous speech recognition in Bahasa Indonesia which has low Word Error Rate and to determine the optimum numbers of training and testing data which minimize the Word Error Rate.]]>
<![CDATA[Pembangkitan Pola Batik dengan Menggunakan Neural Transfer Style dengan Penggunaan Cost Warna ]]>
<![CDATA[Yosef Ariyanto Irawan]]>;
<![CDATA[Andreas Widjaja]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v6i2.2698
<![CDATA[In this research, the neural transfer styles technique was applied to transfer styles of pattern of Batik, a traditional Indonesian cloth painted using the wax-resist dyeing technique, to some certain images. The transfer was performed using a well-known convolutional neural network (CNN) architecture. Some neural transfer tests were done to produce solid color which originally came from color clustered images. The color cost function of the CNN was computed at every epoch of the iterative neural computation. The result of the transfer are images with clustered colors and a slightly apparent color gradient. The produced images can be classified as "Creative Batik".]]>
<![CDATA[Analisis Pengaruh Teks Preprocessing Terhadap Deteksi Plagiarisme Pada Dokumen Tugas Akhir ]]>
<![CDATA[Ariel Elbert Budiman]]>;
<![CDATA[Andreas Widjaja]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 3 (2020): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v6i3.2892
<![CDATA[Final Project Report at a university has the potential for plagiarism. To detect possible plagiarism, String Similarity can be used. Text preprocessing is needed to process words which can make String Similarity results inaccurate. The value of the distribution of the results of the similarity that is getting higher shows the level of accuracy is also getting higher. Reports that contain many words can make it difficult to find plagiarism recommendations. In this study, we try to divide the report into each chapter to provide more detailed recommendation material. By using text preprocessing and comparison methods in the same chapter, can determine the characteristics of each chapter. The discovery of the characteristics of each chapter can be used as plagiarism recommendation material in more detail than a full text report. The experiment was a comparison of the results of cosine similarity between the same chapters and full text, then combined with preprocessing stopword removal and stemming. The experimental results show that the use of preprocessing stopword removal and stemming can produce the highest distribution value and the similarity ratio in each chapter can show its characteristics. Words that represent the characteristics of a chapter can potentially become a stopword.]]>
<![CDATA[Deteksi Buah Menggunakan Supervised Learning dan Ekstraksi Fitur untuk Pemeriksa Harga ]]>
<![CDATA[Kristiawan Kristiawan]]>;
<![CDATA[Deon Diamanta Somali]]>;
<![CDATA[Try Atmaja Linggan jaya]]>;
<![CDATA[Andreas Widjaja]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 3 (2020): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v6i3.3029
<![CDATA[The role of technology in the business world is growing over time. The development of technology, making machines step by step is able to replace the work done by humans. The industrial revolution is a clear example of such technologial development and its use in our daily life. in the fourth industrial revolution that we face today, IOT technology provides the ability of the five senses and think like humans to machines. Over time, human work will be replaced by such technology which provides efficiency like never before. One technology that can provide efficiency is computer vision. In retail context, computer vision can help humans to recognize fruits in supermarkets so that it will help customers do self-service, without having to ask the clerk in the fresh section of the supermarket so that supermarkets can be more efficient and customers can be served better and faster. Computer Vision and machine learning can help retail companies provide self service price checkers for fruit products in supermarkets.]]>
<![CDATA[Continuous Integration and Continuous Delivery Platform Development of Software Engineering and Software Project Management in Higher Education ]]>
<![CDATA[Sendy Ferdian]]>;
<![CDATA[Tjatur Kandaga]]>;
<![CDATA[Andreas Widjaja]]>;
<![CDATA[Hapnes Toba]]>;
<![CDATA[Ronaldo Joshua]]>;
<![CDATA[Julio Narabel]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v7i1.3254
<![CDATA[We present a report of development phase of a platform which aims to enhance the efficiency of software project management in higher education. The platform accommodates a strategy known as Continuous Integration and Continuous Delivery (CI/CD). The phase consists of several stages, followed by testing of the system and its deployment. For starters, the CI/CD platform will be deployed for software projects of students in the Faculty of Information Technology, Universitas Kristen Maranatha. The goal of this paper is to show a design of an effective platform for continuous integration and continuous delivery pipeline to accommodate source code compilation, code analysis, code execution, until its deployment, all in a fully automated fashion.]]>
<![CDATA[Exploratory Data Analysis terhadap Kepadatan Penumpang Kereta Rel Listrik ]]>
<![CDATA[Feliks Victor Parningotan Samosir]]>;
<![CDATA[Loudry Palmarums Mustamu]]>;
<![CDATA[Erik Dwi Anggara]]>;
<![CDATA[Albertus Indarko Wiyogo]]>;
<![CDATA[Andreas Widjaja]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 2 (2021): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v7i2.3700
<![CDATA[The existence of the Kereta Rel Listrik Commuter Line as the backbone of transportation in the Jakarta - Bogor - Depok - Tangerang - Bekasi - Banten area has a very important role for commuter mobility around Daerah Khusus Ibukota Jakarta. With an average number of 1.1 million passengers per day, Kereta Rel Listrik is one of the factors supporting Indonesia's economy and growth in various sectors. On the other hand, the Covid-19 pandemic that hit the world caused restrictions on human movement which resulted in a decline in all economic sectors. The purpose of this research is to obtain optimal train schedule recommendations for the operation of the Kereta Rel Listrik Commuter Line in the Rangkasbitung - Tanah Abang service to carry passengers optimally while adhering to the physical distancing protocol set by the Minister of Transportation to prevent the wider spread of Covid-19. With such a large amount of data that must be processed, Exploratory Data Analysis is one of the choices we take to process the above data to get satisfactory results.]]>
<![CDATA[High Performance Computing Environment using General Purpose Computations on Graphics Processing Unit ]]>
<![CDATA[Andreas Widjaja]]>;
<![CDATA[Tjatur Kandaga Gautama]]>;
<![CDATA[Sendy Ferdian Sujadi]]>;
<![CDATA[Steven Rumanto Harnandy]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 2 (2021): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v7i2.3715
<![CDATA[Here a report of a development phase of an environment of high performance computing (HPC) using general purpose computations on the graphics processing unit (GPGPU) is presented. The HPC environment accommodates computational tasks which demand massive parallelisms or multi-threaded computations. For this purpose, GPGPU is utilized because such tasks require many computing cores running in parallel. The development phase consists of several stages, followed by testing its capabilities and performance. For starters, the HPC environment will be served for computational projects of students and members of the Faculty of Information Technology, Universitas Kristen Maranatha. The goal of this paper is to show a design of a HPC which is capable of running complex and multi-threaded computations. The test results of the HPC show that the GPGPU numerical computations have superior performance than the CPU, with the same level of precision.]]>
<![CDATA[Prediksi Kinerja Pegawai sebagai Rekomendasi Kenaikan Golongan dengan Metode Decision Tree dan Regresi Logistik ]]>
<![CDATA[Erik Dwi Anggara]]>;
<![CDATA[Andreas Widjaja]]>;
<![CDATA[Bernard Renaldy Suteja]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 1 (2022): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v8i1.4479
<![CDATA[Employee performance is one element that greatly determines the quality of an organization, both government and private. Employee performance appraisal has become a routine for most companies. Performance appraisal is required for the process of salary increases, promotions, and demotions. Until this research was carried out, the processing of employee performance appraisal and evaluation at Prasama Bhakti Foundation was still done manually, so that sometimes employee promotions were carried out late or even on an inconsistent basis for each employee. Therefore, it is necessary to group data with the help of machine learning that can help predict the eligibility of an employee to get a promotion based on his performance. Classification is one method for classifying or classifying data that are arranged systematically. Decision tree and logistic regression methods are classification or grouping methods that have been widely used for solving classification problems. In this study, it will be explained how the process of processing employee performance appraisal data starts from data preparation to determine the accuracy of the decision tree model and logistic regression that is formed. The two classification models are used to predict employee performance as a recommendation for employee promotion at the Prasama Bhakti Foundation. ]]>
