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<![CDATA[Future Service in Industry 5.0: Survey Paper ]]>
<![CDATA[Umar Al Faruqi]]>
<![CDATA[ Jurnal Sistem Cerdas Vol. 2 No. 1 (2019): Artificial Intelligence for Smart Society ]]>
Publisher : <![CDATA[APIC ]]>
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DOI: 10.37396/jsc.v2i1.21
<![CDATA[With the rapid development of technology in the digitalization era, Industry 4.0 became a terminology that became a reference for research and development in the field of technology in various sectors. This continues to trigger all people to develop technology to enable better utilization in facilitating human life. Society 5.0 is an idea that explains the revolution in people's lives with the development of the fourth industrial revolution. The concept that wants to be presented is how there is a revolution in society that both utilizing technology and also considering humanities aspects. Some sectors of work and needs are beginning to enter digitalization that utilizes Artificial Intelligence, Big Data, Robotics, Automation, Machine Learning, and the Internet of Things.]]>
<![CDATA[Prediksi Waktu Sandar Kapal Di Pelabuhan Batu Ampar, Kota Batam, Provinsi Kepulauan Riau ]]>
<![CDATA[Saiful Rizal]]>;
<![CDATA[Candra Kurniawan]]>;
<![CDATA[Fahrur Rozi]]>
<![CDATA[ Jurnal Sistem Cerdas Vol. 2 No. 2 (2019): Smart Transportation ]]>
Publisher : <![CDATA[APIC ]]>
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DOI: 10.37396/jsc.v2i2.26
<![CDATA[Pelabuhan Batu Ampar merupakan pelabuhan barang terbesar di Kota Batam yang memiliki lalu lintas tertinggi baik untuk kegiatan ekspor maupun kegiatan impor. Waktu tunggu (dwelling time) masih menjadi masalah dalam layanan pelabuhan. Waktu tunggu merupakan salah satu indikator efisiensi pengelolaan pelabuhan. Rata-rata waktu tunggu pelabuhan Batu Ampar untuk kegiatan bongkar pada triwulan I-2015 adalah 7 hari, sedangkan kegiatan muatnya adalah 5 hari. Hal ini yang menjadikan kinerja pelabuhan Batu Ampar masih banyak dikeluhkan, sehingga berakibat banyaknya antrian kapal. Untuk itu, perlu dilakukan analisis guna menghasilkan model yang bisa memberikan gambaran waktu tunggu di pelabuhan dan melakukan evaluasi terhadap model analitik yang telah dibangun. Analisa data sekunder pelabuhan Batu Ampar menggunakan data mining. Metode data mining yang dilakukan menggunakan algoritma supervised learning, yaitu multiple regression dan decision trees. Tujuan umum dari multiple regression adalah untuk mempelajari lebih lanjut tentang hubungan antara beberapa variabel independen atau prediktor dan variabel dependen atau kriteria. Decision trees yang digunakan untuk eksplorasi data pelabuhan ini menggunakan klasifikasi. Klasifikasi decision trees dapat menemukan apakah data mengandung kelas objek yang dipisahkan dengan baik, sehingga kelas dapat diinterpretasikan secara bermakna dalam konteks teori substantif. Dua metode evaluasi model dilakukan untuk dua hasil permodelan yang dibangun. Uji Analysis of Variance (Anova) digunakan untuk evaluasi model multiple regression, sedangkan untuk model decision tree dievaluasi dengan confussion matrix. Hasil analisis data menunjukkan lamanya waktu kapal melakukan bongkar/muat dipengaruhi oleh tiga variabel yaitu jenis ekspedisi, bendera, dan volume. Dengan menggunakan regresi berganda maka dihasilkan model prediksi waktu sandar kapal. Hasil evaluasi model menunjukkan bahwa model yang dibuat signifikan. Dengan tingkat kepercayaan 95% model prediktif yang dibuat akan merepresentasikan nilai sebenarnya. Untuk decision tree, evaluasi menunjukkan model yang dibuat sudah fit, dengan presisi 84,50%.]]>
<![CDATA[Prediksi Tingkat Kepenuhan Penumpang Pesawat dari Bandara Hang Nadim Batam ]]>
<![CDATA[You Ari Faeni]]>;
<![CDATA[Nyanwar Eko Pribadi]]>;
<![CDATA[Jackson Bobby Romano Daba]]>
<![CDATA[ Jurnal Sistem Cerdas Vol. 2 No. 2 (2019): Smart Transportation ]]>
Publisher : <![CDATA[APIC ]]>
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DOI: 10.37396/jsc.v2i2.28
<![CDATA[Batam is a very strategic area located in the Indonesia Malaysia Singapore Growth Triangle region. Transportation is an important factor in the economic growth of Batam City, especially air transportation. Various types of data are collected, but only a few are analyzed so that they can generate new knowledge. This research will analyze flight data from Hang Nadim Batam airport to predict the fullness of aircraft from Batam to various destinations. The results showed that the support vector machine (SVM) model showed a higher level of accuracy than decision tree in predicting the fullness of the aircraft]]>
<![CDATA[Predictive Analytic Klasifikasi Penentuan Tarif Sewa Bus ]]>
<![CDATA[Arif Rachmat]]>;
<![CDATA[Nuqson Masykur Huda]]>;
<![CDATA[Sri Anita]]>
<![CDATA[ Jurnal Sistem Cerdas Vol. 2 No. 2 (2019): Smart Transportation ]]>
Publisher : <![CDATA[APIC ]]>
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DOI: 10.37396/jsc.v2i2.29
<![CDATA[Currently, the bus rental business has become the choice of consumers in traveling, because of the decision of flexibility and better availability. The government regulates that consumer and business owner agreements determine bus rental rates without routes. In this study intends to do clustering from the history of raw data that already exists before. Data is obtained from companies in the form of spreadsheet files originating from non-information systems. The raw data is combined and normalized, to eliminate the noise data and the data is not abnormal. The clustering results using the K-Means algorithm and Louvain clustering produce several tariff groups that can be used as a reference for determining fare. In this paper also concludes about unbalanced data, which can cause data clustering errors.]]>
<![CDATA[Predictive Model of Passengers Trans Metro Bandung Encouraging Smart Transportation ]]>
<![CDATA[Nova Nurviana]]>;
<![CDATA[Sigit Kusumo Ati]]>;
<![CDATA[Hani Purwati Hanifah]]>
<![CDATA[ Jurnal Sistem Cerdas Vol. 2 No. 2 (2019): Smart Transportation ]]>
Publisher : <![CDATA[APIC ]]>
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DOI: 10.37396/jsc.v2i2.30
<![CDATA[Smart city can be accomplished by merging all components to be smart system. One of significant components is transportation. Everybody moves, everybody need to reach other place safely, efficiency, and comfortably. In order to get smart, we have to analyze the best way to improve capability. One of the best ways is to make predictive model that one may conceive strategies attracting passengers. Using Bayesian dan SVM as comparison will produce best fit model for Trans Metro Bandung passenger data. Better strategy is an emergence needed to produce Trans Metro Bandung better performance because the model predicted there will be no significant growth for Trans Metro Bandung passenger]]>
<![CDATA[Estimasi Tujuan Penumpang Menggunakan Predictive Model dengan Data Smart Card ]]>
<![CDATA[Ekky Alam]]>;
<![CDATA[Inkreswari Hardini]]>;
<![CDATA[Goklas Panjaitan]]>;
<![CDATA[Sita Rosida]]>
<![CDATA[ Jurnal Sistem Cerdas Vol. 2 No. 2 (2019): Smart Transportation ]]>
Publisher : <![CDATA[APIC ]]>
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DOI: 10.37396/jsc.v2i2.31
<![CDATA[Bus Rapid Transit (BRT) is one of the main choices of public transportation that supports mobility of Jakarta community. As one of the main choices of public transportation, BRT should provide good service and always improve its performance. Needs for moving or mobility will cause a problem if the moving itself is heading at the same area and at the same time. That will cause some problems which are often faced in urban areas such as traffic and delay. To overcome those problems there needs to be a strategy to build good public transportation planning, besides need to know individual travel patterns to overcome problems and improve BRT service. In case to realize those plans needs to be built origin-destination (O-D) matrix. O-D matrix is a matrix that each cell is an amount of trip from the source(row) to the destination (column). O-D matrix is beneficial for analysis, design and public transportation management. O-D matrix also provides useful information like amount of trip between 2 different locations, that can be utilized as fundamental information for decision making for three levels of strategic management (long term planning), tactic (service adjustment and network development), and operational (scheduling, passenger statistic, and performance indicator). To build O-D matrix is required a predictive model that can be measured to predict passenger destination. The predictive model will be build using classification algorithms such as Decision Tree and K-Nearest Neighbor (KNN).]]>
<![CDATA[Klustering Jumlah Penumpang pada Halte Bus Rapid Transit Kota Tangerang ]]>
<![CDATA[Erni Dianawati]]>;
<![CDATA[Putri Previa Yanti]]>;
<![CDATA[Yulia Suryandari]]>
<![CDATA[ Jurnal Sistem Cerdas Vol. 2 No. 3 (2019): Transformasi dan Inovasi Kota Cerdas ]]>
Publisher : <![CDATA[APIC ]]>
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DOI: 10.37396/jsc.v2i3.34
<![CDATA[Bus Rapid Transit (BRT) Trans Tangerang is a local public transportation system developed in the main corridor of the Tangerang City area as well as a feeder for Transjabodetabek and rail transport. BRT Trans Tangerang is one part of the Bus Rapid Transit program which is implemented and launched by Department of Transportation. The BRT Trans Tangerang system began operating at the beginning of December 2016. Some problems faced by Trans Tangerang Bus Rapid Transit such as the number of passengers that need to be increased to reduce congestion in the city of Tangerang and the placement of the right bus stops to attract passengers. Therefore, to overcome this problem, data and information about BRT Trans Tangerang are needed to find patterns of passenger distribution. The process of finding this information can be done by using data mining method. Within this method, there is a clustering technique which is useful to clustering all data in groups which have similar data. The algorithm used to classify the data is called simple k-means algorithm. In this study, all the data will be clustered into five cluster. The clustering results have almost the same nature between the data in one cluster, namely the total number of passengers per month, so that by performing clustering characteristics, knowledge of the busy schedule of passengers in one day can help related parties to anticipate the density of passengers on certain days.]]>
<![CDATA[Analisis Kecenderungan Keterlambatan Pembayaran Pengecekan Kapal di Pelabuhan Regional Riau ]]>
<![CDATA[Hespri Yomeldi]]>;
<![CDATA[Moh Roufiq Azmy]]>;
<![CDATA[Ryche Pranita]]>
<![CDATA[ Jurnal Sistem Cerdas Vol. 2 No. 2 (2019): Smart Transportation ]]>
Publisher : <![CDATA[APIC ]]>
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DOI: 10.37396/jsc.v2i2.36
<![CDATA[Ship health checks must be carried out which function to provide a sailing permit. The implementation of ship health checks is carried out in collaboration with the ministry of health and transportation. The implementation of the activity, commonly known as Port Health Quarantine Clearance (PHQC) requires time to check and the ship makes a payment check to be able to issue a sailing permit. The problem that arises in the field is that the ship delays PHQC payments and then impacts on the buildup of ships in the port, besides that officers also need longer time to process the issuance of sailing permits. This of course has an impact on other port services such as dwelling time and scheduled departures that can be delayed. In overcoming this problem, an in-depth study is needed to analyze the trend of late payment of ship health checks, what variables influence it and how treatment is done to overcome these problems. Using logistic regression and decision tree with Classification and Regression Tree algorithm , a model is then developed that determines the variables that affect the delay of the ship making PHQC payments.]]>
<![CDATA[The Performance of Fuzzy Logic How to Fixed The Risk Level of Bug System as Hard-working Quality Software ]]>
<![CDATA[Agus Pamuji]]>
<![CDATA[ Jurnal Sistem Cerdas Vol. 2 No. 2 (2019): Smart Transportation ]]>
Publisher : <![CDATA[APIC ]]>
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DOI: 10.37396/jsc.v2i2.37
<![CDATA[The quality of software production is considered important when testing which is involves several IT Staff such as IT development, operation, end-user. One of the issue was having today is a bug processing where it almost all platforms too difficult to avoid from the bugs and even might be full of the risks. In the main of Our focus is on how to measure and attempt to reduce the number were indicated as bugs from low up to critical levels. Furthermore, we were propose with a method already known as a fuzzy logic approach to measure the severity of the presence of bugs during the testing process. there are 20 thousand even more bugs have been reported and be supposed removed with the fuzzy logic approach with various levels. As The end result is that we have found a gradual 20% reduction in various criteria in the testing process as experimentally. Therefore, fuzzy logic is considered as effective enough to be able to improve existing methods and support to reduce bugs significantly.]]>
<![CDATA[Strategi Transisi Kabupaten Purworejo dari E-Government menuju Smart City ]]>
<![CDATA[Murhadi Murhadi]]>;
<![CDATA[Hamid M. Jumasa]]>
<![CDATA[ Jurnal Sistem Cerdas Vol. 2 No. 3 (2019): Transformasi dan Inovasi Kota Cerdas ]]>
Publisher : <![CDATA[APIC ]]>
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DOI: 10.37396/jsc.v2i3.41
<![CDATA[Starting in 2018 Purworejo district will implement governance by following the smart city system. Purworejo Government has been very serious about the planning and implementation plan of smart cities in Purworejo Regency. In the Regional Medium-Term Development Plan (RPJMD) 2016-2021, which still projects e-government based governance, there is the term cyber city to support the implementation of more effective and efficient city governance. The transition process from e-government to smart city needs to be pursued so that the planning contained in the RPJMD can work in synergy with the Garuda Smart City Model (GSCM) adopted by Purworejo district. The transition method used is to link the programs contained in the RPJMD with the service domain, namely smart economy, smart society, and smart environment.]]>
