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All Journal ComEngApp : Computer Engineering and Applications Journal International Journal of Renewable Energy Development Journal of ICT Research and Applications CESS (Journal of Computer Engineering, System and Science) Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jiko (Jurnal Informatika dan komputer) KOMPUTEK Charity : Jurnal Pengabdian Masyarakat Jurnal Nasional Teknik Elektro dan Teknologi Informasi eProceedings of Engineering Dike International Journal of Renewable Energy Development
Danang Triantoro Murdiansyah
Telkom University
Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Journal : Indonesian Journal on Computing (Indo-JC)

 1 
OPTIMASI JARINGAN SENSOR NIRKABEL MENGGUNAKAN ALGORITMA TWO SUB-SWARMS PSO DISKRIT Danang Triantoro Murdiansyah
Indonesia Journal on Computing (Indo-JC) Vol. 1 No. 1 (2016): March, 2016
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2016.1.1.36

Abstract

Pada paper ini diusulkan sebuah algortima berbasis PSO, yaitu Two Sub-Swarms PSO Diskrit atau disingkat dengan TSS PSO Diskrit, untuk memecahkan masalah konsumsi energi pada jaringan sensor nirkabel. Jarak yang jauh antara sensor nirkabel dan stasiun utama pada jaringan sensor nirkabel dapat menyebabkan energi pada sensor nirkabel cepat habis dan menurunkan umur pakai dari sensor nirkabel tersebut. Untuk memecahkan masalah konsumsi energi tersebut, metode klasterisasi dipilih. Dengan melakukan klasterisasi pada jaringan sensor nirkabel menjadi sejumlah klaster sensor nirkabel, masalah jarak yang jauh untuk transfer data dapat diatasi dan energi yang dibutuhkan oleh sensor nirkabel jauh berkurang. Pada proses klasterisasi akan dipilih sejumlah sensor nirkabel untuk menjadi sensor kepala atau disebut juga dengan cluster head. Simulasi menunjukan bahwa algoritma TSS PSO Diskrit dapat mencapai solusi yang baik dengan cepat dan menghasilkan efisiensi jarak transmisi sampai 95.36% dari transmisi jarak yang ditempuh dengan cara transmisi langsung. Performa algoritma TSS PSO Diskrit ini juga dibandingkan dengan penelitian sebelumnya yang menggunakan AG (Algoritma Genetika) [1].
Optimasi Rute Angkutan Kota Secara Simultan Menggunakan Algoritma Exhaustive Search (Studi Kasus Sepuluh Trayek Kota Bandung) M. Hady Setiawan; Mahmud Imrona; Danang Triantoro Murdiansyah
Indonesia Journal on Computing (Indo-JC) Vol. 2 No. 2 (2017): September, 2017
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2017.2.2.178

Abstract

Angkutan kota merupakan salah satu sarana transportasi yang berfungsi untuk mengangkut penumpang dari tempat asal ke tempat tujuan. Saat ini, masyarakat lebih memilih menggunakan kendaraan pribadi dari pada menggunakan jasa angkutan kota yang disebabkan oleh beberapa faktor, salah satunya yaitu kurangnya ketersebaran rute trayek angkutan kota. Akibatnya penggunaaan kendaraan pribadi terutama kendaraan bermotor melebihi batas wajar sehingga menyebabkan kemacetan. Oleh karena itu, diperlukan optimasi rute trayek angkutan kota untuk mengatasi masalah tersebut. Ada dua sudut pandang yang diperhatikan dalam penelitian ini, yaitu: pemerintah (menginginkan tingkat ketersebaran rute trayek yang tinggi), dan sopir (menginginkan pendapatan yang tinggi). Pada penelitian ini dilakukan optimasi sepuluh trayek angkutan kota menggunakan algoritma exhaustive search dengan memperhatikan ketersebaran rute. Hasil dari penelitian ini menghasilkan peningkatan pendapatan sopir angkutan kota sebesar 57,25%, dan peningkatan ketersebaran rute sebesar 33,2 %.
Implementation of Naïve Bayes and Gini Index for Spam Email Classification Fikri Rozan Imadudin; Danang Triantoro Murdiansyah; Adiwijaya
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 1 (2021): April, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.1.452

Abstract

Email is a medium of information that is still frequently used by people today. At the moment email still has an endless problem that is spam email. Spam email is an email that can pollute, damage or disturb the recipient. In this study, we show the performance and accuracy of Multinomial Naïve Bayes (MNNB) and Complete Gini-Index Text (GIT) for use in spam email filtering. In this study, we used 6 cross-validations as testers for the built classification machines. We found that the average yield can exceed Multinomial Naïve Bayes without using feature selection which only uses 80000 features with a difference of 0.39%. Feature selection also increases speed during classification and can reduce features that are less relevant to the category to be classified.
Implementation of K-Means++ Algorithm for Store Customers Segmentation Using Neo4J Arief Chaerudin; Danang Triantoro Murdiansyah; Mahmud Imrona
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 1 (2021): April, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.1.547

Abstract

In the era of data and information, data has become one of the most useful and desirable things. Data can be useful information if the data is processed properly. One example of the results of data processing in business is by making customer segmentation. Customer segmentation is useful for identifying and filtering customers according to certain categories. Analysis of the resulting segmentation can produce information about more effective target market, more efficient budget, more accurate marketing or promotion strategies, and much more. Since segmentation aims to separate customers into several categories or clusters, a clustering algorithm can be used. In this research, customer segmentation is carried out based on the value of income and value of expenditure. The categorization method that will be used for this research is to use the K-Means ++ algorithm which is useful for determining clusters of the given data. In this study, the implementation of K-Means ++ is carried out using Neo4J. Then in this research, a comparison of K-Means ++ and K-Means is carried out. The result obtained in this study is that K-Means ++ has a better cluster than K-Means in term of silhouette score parameter.
Classification Model of Consumer Question about Motorbike Problems by Using Naïve Bayes and Support Vector Machine Ekky Wicaksana; Danang Triantoro Murdiansyah; Isman Kurniawan
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 2 (2021): September, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.2.561

Abstract

The motorbike plays an important role in supporting daily activity. The motorbike is known as one of the transportation modes that is frequently used in Indonesia. The number of motorbikes used in Indonesia is continuously increasing time by time. Hence, the occurrence of motorbike problems can affect community activity and disturb the economic condition in society. Since the problem of the motorbike can occur at any time, a prevention action is required by providing an online consultation platform. However, a classification model is required to handle a wide range of questions about the motorbike problem. By classifying those questions into a specific class of problems, the solution can be delivered to the consumer faster. In this study, we developed prediction models to classify consumer questions. The data set was collected from consumer questions regarding motorbike problems that are commonly occurring. The model was developed using two machine learning algorithms, i.e., Naïve Bayes and Support Vector Machine (SVM). Text vectorization was performed by using the n-gram and term frequency-inverse document frequency (TF-IDF) method. The results show that the SVM model with the uni-trigram model performs better with the value of accuracy and F-measure, which are 0.910 and 0.910, respectively.
Implementation of Naïve Bayes and Gini Index for Spam Email Classification Imadudin, Fikri Rozan; Murdiansyah, Danang Triantoro; Adiwijaya
Indonesian Journal on Computing (Indo-JC) Vol. 6 No. 1 (2021): April, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.1.452

Abstract

Email is a medium of information that is still frequently used by people today. At the moment email still has an endless problem that is spam email. Spam email is an email that can pollute, damage or disturb the recipient. In this study, we show the performance and accuracy of Multinomial Naïve Bayes (MNNB) and Complete Gini-Index Text (GIT) for use in spam email filtering. In this study, we used 6 cross-validations as testers for the built classification machines. We found that the average yield can exceed Multinomial Naïve Bayes without using feature selection which only uses 80000 features with a difference of 0.39%. Feature selection also increases speed during classification and can reduce features that are less relevant to the category to be classified.
Implementation of K-Means++ Algorithm for Store Customers Segmentation Using Neo4J Chaerudin, Arief; Murdiansyah, Danang Triantoro; Imrona, Mahmud
Indonesian Journal on Computing (Indo-JC) Vol. 6 No. 1 (2021): April, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.1.547

Abstract

In the era of data and information, data has become one of the most useful and desirable things. Data can be useful information if the data is processed properly. One example of the results of data processing in business is by making customer segmentation. Customer segmentation is useful for identifying and filtering customers according to certain categories. Analysis of the resulting segmentation can produce information about more effective target market, more efficient budget, more accurate marketing or promotion strategies, and much more. Since segmentation aims to separate customers into several categories or clusters, a clustering algorithm can be used. In this research, customer segmentation is carried out based on the value of income and value of expenditure. The categorization method that will be used for this research is to use the K-Means ++ algorithm which is useful for determining clusters of the given data. In this study, the implementation of K-Means ++ is carried out using Neo4J. Then in this research, a comparison of K-Means ++ and K-Means is carried out. The result obtained in this study is that K-Means ++ has a better cluster than K-Means in term of silhouette score parameter.
Classification Model of Consumer Question about Motorbike Problems by Using Naïve Bayes and Support Vector Machine Wicaksana, Ekky; Murdiansyah, Danang Triantoro; Kurniawan, Isman
Indonesian Journal on Computing (Indo-JC) Vol. 6 No. 2 (2021): September, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.2.561

Abstract

The motorbike plays an important role in supporting daily activity. The motorbike is known as one of the transportation modes that is frequently used in Indonesia. The number of motorbikes used in Indonesia is continuously increasing time by time. Hence, the occurrence of motorbike problems can affect community activity and disturb the economic condition in society. Since the problem of the motorbike can occur at any time, a prevention action is required by providing an online consultation platform. However, a classification model is required to handle a wide range of questions about the motorbike problem. By classifying those questions into a specific class of problems, the solution can be delivered to the consumer faster. In this study, we developed prediction models to classify consumer questions. The data set was collected from consumer questions regarding motorbike problems that are commonly occurring. The model was developed using two machine learning algorithms, i.e., Naïve Bayes and Support Vector Machine (SVM). Text vectorization was performed by using the n-gram and term frequency-inverse document frequency (TF-IDF) method. The results show that the SVM model with the uni-trigram model performs better with the value of accuracy and F-measure, which are 0.910 and 0.910, respectively.
Co-Authors Aditya Pratama, Rangga Adiwijaya Aldi Riyadi Ramadhan Ali Helmut Andrian Yoga Pratama Annisa Aditsania Arief Chaerudin Aura Sukma Andini Barini Harahap Berlian Kaida Palma Chaerudin, Arief Dany Dwi Prayoga Dede Tarwidi Della Alfarydy Akbar Devi Pratami Dwi Nugroho Dwi Yanita Apriliyana Ekky Wicaksana Emir Husni Emir Husni, Emir Favian Dewanta Felix Octavianus Hasudungan Ferdian Yulianto fh Nhita Fhira Nhita Fikri Rozan Imadudin Gege Safet Yanto Raharjo Gery Nugroho Haditira, Ragil Haga Simada Ginting Hana Rufaidha Ika Arum Puspita Imadudin, Fikri Rozan Isman Kurniawan Jodi Noordiansyah Jondri Jondri Kemas M Lhaksmana Kemas Muslim Lhaksmana Kemas Rahmat Saleh Wiharja Litasari Widyastuti, Litasari Lukito Agung Waskito M Ikhsan Perdana Putra M. Hady Setiawan Mahendra, Muhammad Hafizh Mahmud Imrona Merry Sofhia Tambunan Miranti Andhita Scantya Muhamad Paisal Hanip Muhammad Ramadhan Gandidi Muhammad Zakiyullah Romdlony Narwan Ginanjar Narwan Ginanjar, Narwan Nida Mujahidah Azzahra Nurul Ikhsan Ragil Haditira Reza Rendian Septiawan Rian Febrian Umbara Sahlya Handayati Sugeng Hadi Wirasna Tiwa Ramdhani Umar Ali Ahmad Wicaksana, Ekky Widi Astuti Widi Astuti Youga Pratama Yuwantoro Mukhlisin ZK Abdurahman Baizal