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All Journal Jurnal Informatika Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknik ITS IPTEK Journal of Proceedings Series IPTEK The Journal for Technology and Science Majalah Ilmiah Matematika Komputer Jurnal Buana Informatika JUTI: Jurnal Ilmiah Teknologi Informasi International Journal of Advances in Intelligent Informatics Jurnal Teknobisnis INFORMAL: Informatics Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi EMITTER International Journal of Engineering Technology Journal of ICSAR Math Didactic: Jurnal Pendidikan Matematika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Eksplora Informatika SISFO International Journal of Supply Chain Management CCIT (Creative Communication and Innovative Technology) Journal Indonesian Journal of Electrical Engineering and Computer Science Makara Journal of Technology Sewagati JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)

Buliali, Joko Lianto

Departemen Teknik Informatika, Institut Teknologi Sepuluh Nopember, Kampus ITS, Keputih, Sukolilo, Surabaya 60111

Author-ID : 244737

Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

Published : 45 Documents Claim Missing Document

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Deteksi Jenis Kendaraan di Jalan Menggunakan OpenCV Alvin Lazaro; Joko Lianto Buliali; Bilqis Amaliah
Jurnal Teknik ITS Vol 6, No 2 (2017)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Jenis kendaraan yang melewati suatu ruas jalan dapat diketahui secara komputasi dengan mencocokkan fitur kendaran yang terdeteksi dengan fitur kendaraan standar masing-masing jeniskendaraan. Dengan mencapture frame image yang memuat kendaraan di jalan, OpenCV dapat mencocokkan fitur kendaraan tersebut dengan fitur kendaraan standar masing-masing jeniskendaraan, sehingga jenis kendaraan pada frame image dapat diketahui.

Deteksi Kecepatan Kendaraan Berjalan di Jalan Menggunakan OpenCV Andrew Andrew; Joko Lianto Buliali; Arya Yudhi Wijaya
Jurnal Teknik ITS Vol 6, No 2 (2017)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Saat ini, di berbagai kota telah dipasang CCTV pada setiap ruas jalan. Dari CCTV, dapat diketahui kondisi lalu lintas, namun tidak dapat diketahui kecepatan setiap kendaraaan. Oleh karena itu, dibuat perangkat lunak yang dapat mendeteksi kecepatan kendaraan di ruas jalan dari video yang diambil oleh CCTV. Tujuan lainnya adalah untuk mengetahui perbedaan hasil deteksi kecepatan dengan berbagai nilai FPS (Frame Per Second).Input untuk aplikasi ini adalah video (.avi). Pertama, sistem mengambil Region of Interest (ROI). Selanjutnya, sistem melakukan background subtraction, membuat garis awal dan akhir, memperbarui posisi kendaraan, dan menyimpan hasil kecepatan rata-rata kendaraan ke berkas Excel (.xls).Skenario uji coba dilakukan berdasarkan nilai FPS pada video (30 FPS, 27 FPS, 25 FPS, dan 20 FPS). Setiap skenario terdapat sub-skenario berdasarkan posisi koordinat garis akhir {(296,0); (282,0); (270,0); dan (248,0)}. Pengujian dilakukan 5 kali setiap skenario, lalu dibandingkan dengan hasil sebenarnya untuk mendapatkan nilai error pada sistem. Error terkecil yang dihasilkan sistem sebesar 2,75% dengan posisi koordinat garis akhir di (282,0) pada skenario 30 FPS.

Pemodelan Multilabel Tweet Media Sosial Mahasiswa untuk Klasifikasi Keluhan Muhammad Faris Musthafa; Joko Lianto Buliali; Victor Hariadi
Jurnal Teknik ITS Vol 7, No 1 (2018)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Pada umumnya sistem informasi akademik di sebuah perguruan tinggi memiliki fitur umum bagi dosen untuk memantau proses perkembangan akademik anak walinya secara aktif. Namun jika dosen wali ataupun orang tua tidak melakukan pantauan secara aktif maka mahasiswa wali yang memiliki permasalahan akademik berisiko drop out dalam proses evaluasi tingkat 1 universitas karena rendahnya pemahaman dosen terhadap mahasiswa walinya. Tujuan dari penelitian ini adalah membuat rancangan model deteksi keluhan dalam data tweet mahasiswa. Aspek keluhan bisa dibagi mennjadi empat kategori: keluhan personal, keluhan subjek, keluhan relasi, dan keluhan institusi. Metode multilabel yang digunakan adalah Binary Relevance dengan pilihan classifier Naïve Bayes, Simple Logistic, KStar, Decision Table, dan j48. Berdasarkan hasil pengujian ada berbagai classifier, Naïve Bayes memiliki performa tertinggi baik dalam aspek akurasi maupun waktu eksekusi. Hasil implementasi sistem deteksi multilabel keluhan menggunakan classifier Naïve Bayes pada delapan puluh data uji yterhadap label keluhan personal, subjek, relasi, dan institusi memiliki akurasi masing-masing bernilai 76.47%, 75%, 80%, dan 80%. Hasil deteksi multilabel keluhan yang ditemukan berpotensi digunakan lebih lanjut pada konteks yang lebih luas

Anomaly detection on flight route using similarity and grouping approach based-on automatic dependent surveillance-broadcast Mohammad Yazdi Pusadan; Joko Lianto Buliali; Raden Venantius Hari Ginardi
International Journal of Advances in Intelligent Informatics Vol 5, No 3 (2019): November 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i3.232

Flight anomaly detection is used to determine the abnormal state data on the flight route. This study focused on two groups: general aviation habits (C1)and anomalies (C2). Groups C1 and C2 are obtained through similarity test with references. The methods used are: 1) normalizing the training data form, 2) forming the training segment 3) calculating the log-likelihood value and determining the maximum log-likelihood (C1) and minimum log-likelihood (C2) values, 4) determining the percentage of data based on criteria C1 and C2 by grouping SVM, KNN, and K-means and 5) Testing with log-likelihood ratio. The results achieved in each segment are Log-likelihood value in C1Latitude is -15.97 and C1Longitude is -16.97. On the other hand, Log-likelihood value in C2Latitude is -19.3 (maximum) and -20.3 (minimum), and log-likelihood value in C2Longitude is -21.2 (maximum) and -24.8 (minimum). The largest percentage value in C1 is 96%, while the largest in C2 is 10%. Thus, the highest potential anomaly data is 10%, and the smallest is 3%. Also, there are performance tests based on F-measure to get accuracy and precision.

PERANCANGAN DAN ANALISIS BIAYA-MANFAAT SISTEM SUPPLIER RELATIONSHIP MANAGEMENT (SRM) DI JOINT OPERATING BODY (J.O.B) PERTAMINA-PETROCHINA EAST JAVA Hadi Siswidiastono; Joko Lianto Buliali
Jurnal Teknobisnis Vol 1, No 1 (2005): Jurnal TEKNOBISNIS
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat- Institut Teknologi Sepuluh Nopember

As a company which has always collaborate with suppliers, JOB Pertamina-PetroChina is well aware of the importance of suppliers’ role to support the company business process, which places them as a major force that need to be considered to achieve competitive advantages. Supplier satisfaction and loyalty supported by supplier performance evaluation is several important elements to support company’s business existence.In order to keep good relationship with suppliers, company faces several problems. Currently, there is no system available to manage suppliers’ database and its procurement. The purpose of this thesis is to design a Supplier Relationship Management for JOB P-PEJ followed by cost-benefit analysis. This new system is expected to help company to manage their supplier better in the future.The outcome expected from this study is a design of Supplier Relationship Management for JOB P-PEJ that is capable to determine most profitable supplier, to identify suppliers, and to manage complaints. Cost-benefit analysis will be done to evaluate whether the new system is useful to support Material and Logistic functions in managing suppliers.

Metode Hibrida K-Means dan Generalized Regression Neural Network Untuk Prediksi Arus Lalu Lintas Saprina Mamase; Joko Lianto Buliali
Jurnal Buana Informatika Vol. 7 No. 3 (2016): Jurnal Buana Informatika Volume 7 Nomor 3 Juli 2016
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v7i3.654

Abstract. Traffic flow forecasting is a popular research topic in the development of Intelligent Transportation System. There have been many forecasting methods used for traffic flow forecasting, such as Generalized Regression Neural Network (GRNN) which has a fairly good accuracy. One of the GRNN’s characteristics is that the number of neurons in pattern layer increases as the number of training samples raise and this can cause overfitting problem. In this research, a hybrid method to predict traffic flow is proposed, that is K-means and GRNN algorithm. K-means method aims to solve overfitting problem in GRNN model by choosing training samples based on their similar characteristics. Leave One Out Cross Validation (LOOCV) is used to select an appropriate smoothing factor parameter at each GRNN’s model. Mean Absolute Percentage Error (MAPE) is used as the evaluation criterion in the testing process. The results show that the proposed method could improve the accuracy of predictions by reducing the value of MAPE by 0.82-3.81%.Keywords: Traffic flow forecasting, K-means, Generalized Regression Neural Network, Leave One Out Cross ValidationAbstrak. Prediksi arus lalu lintas telah menjadi tren topik penelitian untuk pengembangan sistem transportasi cerdas. Telah banyak metode yang digunakan terkait prediksi arus lalu lintas, diantaranya yaitu Generalized Regression Neural Network (GRNN) yang memiliki akurasi yang cukup baik. Salah satu karakteristik GRNN adalah jumlah neuron pada pattern layer akan bertambah seiring meningkatnya jumlah data latih yang akan mengakibatkan masalah overfitting. Dalam penelitian ini diusulkan metode hibrida K-means dan GRNN untuk prediksi arus lalu lintas. Metode K-means bertujuan untuk mengatasi masalah overfitting pada model GRNN dengan memilih data latih berdasarkan kemiripan karateristiknya. Algoritma Leave One Out Cross Validation (LOOCV) digunakan untuk memilih parameter smoothing factor terbaik pada setiap model GRNN. Mean Absolute Percentage Error (MAPE) digunakan sebagai kriteria evaluasi model prediksi. Hasil menunjukkan bahwa metode yang diusulkan dapat meningkatkan akurasi prediksi dengan penurunan nilai MAPE sebesar 0,82-3,81%.Kata Kunci: Prediksi arus lalu lintas, K-means, Generalized Regression Neural Network, Leave One Out Cross Validation

Optimasi Waktu Lampu Pengatur Lalu Lintas Menggunakan Algoritma Genetika di Persimpangan Heru Tri Ahmanto; Joko Lianto Buliali
INFORMAL: Informatics Journal Vol 2 No 3 (2017): INFORMAL - Informatics Journal
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

In the modern era of population growth is increasing, the increase in the number of population then increased also the means of transportation that causes traffic congestion. intersection are the concentration of traffic issues that become one of the main causes of traffic congestion, this is because the intersection is where the migration of cars from one to the other road segments. Traffic lights are the lights used to curb road users crossing a road junction, but most traffic lights do not run optimally, causing traffic congestion. In this study proposed a method of genetic algorithm for optimization of the traffic lights with the aim of getting the model movement of car and get the fitness function model for the optimization of traffic light so as to get traffic lights are optimal on each road segment base on total average number of cars were able to pass through the intersection . Base on scenarios testing 1, produces lights optimal on each road segment, namely roads 1 and roads 3 for 49 seconds for green time, roads 2 and the road 4 for 55 seconds for green time means total average number of cars passing through the intersection of as many as 81 cars and Base on scenarios testing 2, produces lights optimal on each road segment, namely roads 1 and roads 3 for 56 seconds for green time, roads 2 and the road 4 for 54 seconds for green time means total average number of cars passing through the intersection of as many as 95 cars.

Implementasi Particle Swarm Optimization pada K-Means untuk Clustering Data Automatic Dependent Surveillance-Broadcast Achmad Saidul; Joko Lianto Buliali
Jurnal Eksplora Informatika Vol 8 No 1 (2018): Jurnal Eksplora Informatika
Publisher : Bagian Perpustakaan dan Publikasi Ilmiah - Institut Teknologi dan Bisnis STIKOM Bali

Investigasi kecelakaan penerbangan di Indonesia pada tahun 2010 sampai 2016 sebesar 212 investigasi. Hal tersebut dapat dihindari apabila ada sistem penerbangan yang dapat memastikan penerbangan berjalan aman, seperti sistem lalu lintas udara yang dapat mendeteksi apabila pesawat bergerak menuju ke arah yang salah. Penelitian ini bertujuan untuk mengelompokkan rute penerbangan pada data Automatic Dependent Surveillance-Broadcast menggunakan metode clustering untuk mendapatkan similaritas rute penerbangan. Penulis mengusulkan metode particle swarm optimization untuk mengoptimalkan metode k-means, yang berguna untuk menentukan titik centroid awal dengan silhouette coefficient sebagai fitness function. Hasil dari penelitian ini menghasilkan zona terbang berdasarkan kebiasaan sehingga dapat digunakan sebagai panduan penerbangan. Pengujian dilakukan dengan membandingkan nilai Davies-Bouldin index dengan metode k-means, k-medoids dan fuzzy c-means. Pada uji coba yang dilakukan, metode yang diusulkan menjadi kelompok metode terbaik pada lima dari enam segmen yang ada serta menghasilkan nilai Davies-Bouldin index lebih baik pada satu segmen sebesar 0,779.

Optimum partition in flight route anomaly detection Mohammad Yazdi Pusadan; Joko Lianto Buliali; Raden Venantius Hari Ginardi
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 3: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i3.pp1315-1329

Anomaly detection of flight route can be analyzed with the availability of flight data set. Automatic Dependent Surveillance (ADS-B) is the data set used. The parameters used are timestamp, latitude, longitude, and speed. The purpose of the research is to determine the optimum area for anomaly detection through real time approach. The methods used are: a) clustering and cluster validity analysis; and b) False Identification Rate (FIR). The results archieved are four steps, i.e: a) Build segments based on waypoints; b) Partition area based on 3-Dimension features P1 and P2; c) grouping; and d) Measurement of cluster validity. The optimum partition is generated by calculating the minimum percentage of FIR. The results achieved are: i) there are five partitions, i.e: (n/2, n/3, n/4, n/5) and ii) optimal partition of each 3D, that is: for P1 was five partitions and the P2 feature was four partitions

Analogy Method Development for Cost Estimation of Software Design Sarno, Riyanarto; Buliali, Joko Lianto; Maimunah, Siti
Makara Journal of Technology Vol. 6, No. 2
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

The important aspect of planning and managing software development project is to estimate the cost of a project. There are several methods for estimating the cost of a software development project, and the Analogy method is a method which gives relatively better estimates. This paper shows that the modified Analogy method selects a closer project reference, estimates more accurate project effort and cost. This study enhances the cost estimate technique by including valid and complete cost parameters, therefore the estimate of a project cost is better than the result of the standard Analogy method.

Co-Authors A.A. Ketut Agung Cahyawan W Achmad Saidul Agri Kridanto Agri Kridanto, Agri Ahmad Saikhu Ahmad Saikhu Alvin Lazaro Andreas Handojo Andreast Wahyu Sugiyarta Andrew Andrew Andriyani Andriyani Andriyani Andriyani Andriyani Arlianti, Irma Arya Yudhi Wijaya Arya Yudi Wijaya Arya Yudi Wijaya Bilqis Amaliah Bisono, Eva Firdayanti Cempaka A. Swastyastu Choirul Mahfud Darlis Herumurti Diagnosa Fenomena Diana Purwitasari Dwi Sunaryono Erma Suryani Fadilah Fahrul Hardiansyah, Fadilah Fahrul Fenky Sugiarto Frica Salim Wiharjo Ginardi, Raden Venantius Hari Giri Wiriapradja Hadi Siswidiastono Harseno, Ahmad Rieskha Hartono Hartono Heru Tri Ahmanto Ilham Gurat Adillion Isye Arieshanti Jingga, Azharine Purwa Juniastuti, Susi Kartika Nuswantara Mohammad Yazdi Pusadan Muchtar Aditya Pradana Muhamad Hilmi Muhammad Faris Musthafa Muhammad Noor Muhammad Zuber Myrna ermawati Pradana, Muhamad Hilmil Muchtar Aditya Prayuda, Janandra Aji Qolby, Lailly Syifa'ul Ratna Rintaningrum, Ratna Reza Prasetya Prayogo Ria Andriana Riyanarto Sarno Saifulloh, Mohamad Saprina Mamase Savitri, Eka Dian Siti Maimunah Suhadi Lili Suhadi Lili Tora Fahrudin Tsabbit Aqdami Mukhtar, Tsabbit Aqdami Umi Trisyanti, Umi Untoro, Meida Cahyo Victor Hariadi Wahyudi Agustiono Waskitho Wibisino Waskitho Wibisono Wijaya, Arya Yudi Wulandari, Desi Yanuar Risah Prayogi Yudhi Purwananto Yudhiakto Pramudya Yuhana, Umi Laili Yuniarto, Muhammad Nur

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