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<![CDATA[KLASIFIKASI PENYAKIT MATA KATARAK BERDASARKAN KELAINAN PATOLOGIS DENGAN MENGGUNAKAN ALGORITMA LEARNING VECTOR QUANTIZATION ]]>
<![CDATA[Hariyanto, Rudi]]>;
<![CDATA[Basuki, Achmad]]>;
<![CDATA[Hasanah, Rini Nur]]>
<![CDATA[ Network Engineering Research Operation [NERO] Vol 2, No 3 (2016): NERO ]]>
Publisher : <![CDATA[Universitas Trunojoyo Madura ]]>
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<![CDATA[Katarak merupakan salah satu jenis kerusakan mata yang menyebabkan lensa mata berselaput, rabun yang bervariasi sesuai tingkatannya hingga menjadi kebutaan. Penyakit katarak ini menggerogoti mata secara perlahan, sedikit demi sedikit tanpa rasa sakit yang dialami pasien tetapi jika penanganannya terlambat maka mengakibatkan kebutaan permanen. Ketepatan penentuan jenis dan letak katarak secara dini sangat penting untuk mencegah dampak keparahan katarak yang lebih parah. Prosedur utama diagnosis katarak (Gold Standart Prosedur) dilakukan menggunakan Computed Tomography ( CT ) scan dan Magnetic Resonance Imaging (MRI). Alternatif diagnosis dapat dilakukan melalui pemeriksaan fisik, pemeriksaan laboratorium, riwayat penyakit, serta informasi lain yang terkait. Tujuan penulisan ini menyajikan hasil kajian mengenai implementasi metode Learning Vector Quantization (LVQ) untuk memudahkan klasifikasi penentuan jenis penyakit katarak serta tingkat keparahannya. Hasil penelitian menunjukkan bahwa penggunaan metode LVQ memberikan tingkat akurasi penentuan sebesar (99%) serta durasi waktu pelatihan (training) sampel sebesar (0,06 detik).Kata Kunci: Katarak, Klasifikasi, Learning Vector Quantization]]>
<![CDATA[Analisis Performansi Raspberry-Pi/Aptus Box Sebagai Portable Server MOOCS ]]>
<![CDATA[Basuki, Achmad]]>;
<![CDATA[Akbar, Sabriansyah Rizkika]]>;
<![CDATA[Setyawan, Raden Arief]]>
<![CDATA[ Jurnal EECCIS Vol 12, No 2 (2018) ]]>
Publisher : <![CDATA[Fakultas Teknik, Universitas Brawijaya ]]>
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<![CDATA[Dewasa ini pemanfaatan e-learning sebagai sarana pembelajaran telah sangat massif. Berkembangnya internet memberikan berbagai alternatif sarana pembelajaran bagi setiap orang. Namun sayangnya kemudahan ini tidak dapat dinikmati oleh masyarakat di area terpencil yang tidak memiliki akses internet. Salah satu cara agar masyarakat tersebut tetap dapat menikmati pembelajaran melalui e-learning adalah menggunakan portable server. Penelitian ini mencoba menganalisa penggunaan Raspberry Pi, sebuah minicomputer, sebagai portable server untuk e-learning.]]>
<![CDATA[Bus Arrival Prediction – to Ensure Users not to Miss the Bus ]]>
<![CDATA[Lutfi Fanani]]>;
<![CDATA[Achmad Basuki]]>;
<![CDATA[Deron Liang]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 2: April 2015 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v5i2.pp333-339
<![CDATA[Predicting arrival times of buses is a key challenge in the context of building intelligent public transportation systems. The bus arrival time is the primary information for providing passengers with an accurate information system that can reduce passenger waiting times. In this paper, we used the normal distribution method to the random of travel times data in a bus line number 243 in Taipei area. In developing the models, data were collected from Taipei Bus Company. A normal distribution method used for predicting the bus arrival time in bus stop to ensure users not to miss the bus, and compare the result with the existing application. The result of our experiment showed that our proposed method has a better prediction than existing application, with the probability user not to miss the bus in peak time is 93% and in normal time is 85%, greater than from the existing application with the 65% probability in peak time, and 70% in normal time.]]>
<![CDATA[The Extended Dijkstra’s-based Load Balancing for OpenFlow Network ]]>
<![CDATA[Widhi Yahya]]>;
<![CDATA[Achmad Basuki]]>;
<![CDATA[Jehn Ruey Jiang]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 2: April 2015 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v5i2.pp289-296
<![CDATA[This paper proposes load-balancing algorithm on the basis of the Extended Dijkstra’s shortest path algorithm for Software Defined Networking (SDN). The Extended Dijkstra’s algorithm considers not only the edge weights, but also the node weights to find the nearest server for a requesting client. The proposed algorithm also considers the link load in order to avoid congestion. We use Pyretic to implement the proposed algorithm and compare it with related ones under the Abilene network topology with the Mininet emulation tool. As shown by the comparisons, the proposed algorithm outperforms the others in term of the network end-to-end latency, throughput and response time at the expense of a little heavier computation load and more memory usage on the SDN controller.]]>
<![CDATA[Design of Pervasive Discovery, Service and Control for Smart Home Appliances: An Integration of Raspberry Pi, UPnP Protocols and Xbee ]]>
<![CDATA[Sabriansyah Rizqika Akbar]]>;
<![CDATA[Maystya Tri Handono]]>;
<![CDATA[Achmad Basuki]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 2: April 2017 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v7i2.pp1012-1022
<![CDATA[Pervasive technology is an important feature in smart home appliances control. With pervasive technology, the user is able to discover and control every device and each service without initialization configuration and setup. Since single-board computer often used in smart home appliances, combining pervasive technology and microcomputer/single-board computer will be important to be applied and make a possibility to create a smart home system based on the requirement of it users that will be beneficial for the smart home users and the developers. This paper proposed a design of pervasive discovery, service, and control system for smart home appliances by integrating Raspberry Pi, UPnP protocols, and Xbee that able to control an RGB LED services such as switching, dimming, change color and read a temperature sensor as an example in smart home appliances. This paper enriched the raspberry Pi GPIO function to be able to control via TCP/IP network with UPnP protocol and receive information from a temperature sensor node via Xbee communication. Service control time is measured with UPnP round trip time by subtracting HTTP response arrival with HTTP request time. GPIO processing time measured at the application level by counting a timer that starts before GPIO process and ended after GPIO successfully executed.]]>
<![CDATA[Identifying Citronella Plants From UAV Imagery Using Support Vector Machine ]]>
<![CDATA[Candra Dewi]]>;
<![CDATA[Achmad Basuki]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v16i4.7450
<![CDATA[High-resolution imagery taken from Unmanned Aerial Vehicle (UAV) is now often used as an alternative in monitoring the agronomic plants compared to satellite imagery. This paper presents a method to identify Citronella among other plants based on UAV imagery. The method utilizes Support Vector Machine (SVM) to classify Citronella among other plants according to the extraction of texture feature. The implementation of the method was evaluated using two group of datasets: 1) consists of Citronella, Kaffir Lime, other green plants, vacant soil, and buildings, and 2) consists of Citronella and paddy rice plants. The evaluation results show that the proposed method can identify Citronella on the first group of datasets with an accuracy 94.23% and Kappa value 88.48%, whereas on the second group of datasets with an accuracy 100% and Kappa value 100%.]]>
<![CDATA[Home Appliance Control with Publish Subscribe in Social Media ]]>
<![CDATA[Sabriansyah Rizqika Akbar]]>;
<![CDATA[Eko Setiawan]]>;
<![CDATA[Achmad Basuki]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 2: June 2015 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v13i2.1309
<![CDATA[ Nowadays, Internet social media has enriched the way people to communicate and interact each other. Will it be possible for people to interact with their home appliances around? This paper proposes a new approach in smart home system that made possible for people to remotely interact with their appliances using social media networks. In this paper, we present a smart home prototype system that leverages Twitter’s Application Program Interface (API) to remotely control home appliances over the Internet. Experiment results showed that the system immediately responds to remote commands sent over a social media account to control home appliances. The system responds the command in 3672.96 ms. Publish-subscribe method work better in mass announcement communication system. Home system could notice all householders in less than 6 s independenly from number of householder. Our proposed method gives alternative solution to build reliable, fast and simple control method.]]>
<![CDATA[Klasifikasi Penyakit Mata Katarak berdasarkan Kelainan Patologis dengan menggunakan Algoritma Learning Vector Quantization ]]>
<![CDATA[Rudi Hariyanto]]>;
<![CDATA[Achmad Basuki]]>;
<![CDATA[Rini Nur Hasanah]]>
<![CDATA[ Journal of Mechanical Engineering and Mechatronics Vol 1, No 02 (2016): Journal of Mechanical Engineering and Mechatronics ]]>
Publisher : <![CDATA[President University ]]>
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DOI: 10.33021/jmem.v1i02.95
<![CDATA[Cataract is one type of eye damage which causes the lens of the eye, nearsightedness which varies according to the level becomes blindness. Cataract eye disease is eating slowly, little by little without the pain experienced by patients but if handled too late then lead to permanent blindness. Eyepiece contains 65% water, 35% protein and the rest are minerals. With increasing age, size and mineral density increases. The accuracy of the determination of the type and location of early cataract is very important to prevent the severity of the impact of more severe cataracts. The main procedure of cataract diagnosis (Gold Standard procedure) was performed using computed tomography (CT) scan and Magnetic Resonance Imaging (MRI). Alternative diagnosis can be made through physical examination, laboratory tests, medical history, and other relevant information. The purpose of this paper presents the results of a study on the implementation of the method of Learning Vector Quantization (LVQ) to facilitate the determination of the classification of types of cataract disease and its severity. The results showed that the use LVQ provide the level of accuracy of the determination of the amount of (99%) as well as the duration of training (training) sample of (0.06 seconds).]]>
<![CDATA[Pengendalian Kemacetan Jaringan Melalui Per-Flow Multipath Routing ]]>
<![CDATA[Fransiska Sisilia Mukti]]>;
<![CDATA[Achmad Basuki]]>;
<![CDATA[Onny Setyawati]]>
<![CDATA[ Jurnal EECCIS Vol 12, No 1 (2018) ]]>
Publisher : <![CDATA[Fakultas Teknik, Universitas Brawijaya ]]>
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<![CDATA[Kemacetan terjadi ketika jumlah paket yang ditransmisikan melalui jaringan telah mendekati kapasitas penanganan paket jaringan. Hal ini menjadi faktor utama untuk diketahui lebih dini, guna menghindari adanya kegagalan dalam proses routing. Sebagian besar protokol routing yang digunakan saat ini menggunakan algoritma yang menghasilkan jalur tunggal saja, tanpa memperhatikan permintaan trafik yang bersifat fluktuatif. Penggunaan jalur tunggal dapat menyebabkan terjadinya kemacetan yang berdampak kepada pemborosan sumber daya. Penelitian ini bertujuan untuk melakukan pengendalian kemacetan jaringan dengan mengkombinasikan mekanisme multipath routing dan congestion control pada protokol routing OSPF. Hasil pengujian menunjukkan keunggulan sistem dalam tiga aspek, yaitu kestabilan nilai throughput (100%) sehingga tidak ada packet loss, pengiriman data 50% lebih cepat, dan utilisasi jaringan yang lebih baik.]]>
