Articles
<![CDATA[Increasing Smoke Classifier Accuracy using Naïve Bayes Method on Internet of Things ]]>
<![CDATA[Putrada, Alieja Muhammad]]>;
<![CDATA[Abdurohman, Maman]]>;
<![CDATA[Putrada, Aji Gautama]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 4, No 1, February 2019 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v4i1.704
<![CDATA[This paper proposes fire alarm system by implementing Naïve Bayes Method for increasing smoke classifier accuracy on Internet of Things (IoT) environment. Fire disasters in the building of houses are a serious threat to the occupants of the house that have a hazard to the safety factor as well as causing material and non-material damages. In an effort to prevent the occurrence of fire disaster, fire alarm system that can serve as an early warning system are required. In this paper, fire alarm system that implementing Naïve Bayes classification has been impelemented. Naïve Bayes classification method is chosen because it has the modeling and good accuracy results in data training set. The system works by using sensor data that is processed and analyzed by applying Naïve Bayes classification to generate prediction value of fire threat level along with smoke source. The smoke source was divided into five types of smoke intended for the classification process. Some experiments have been done for concept proving. The results show the use of Naïve Bayes classification method on classification process has an accuracy rate range of 88% to 91%. This result could be acceptable for classification accuracy. ]]>
<![CDATA[Increasing The Precision Of Noise Source Detection System using KNN Method ]]>
<![CDATA[Nando, Parlin]]>;
<![CDATA[Putrada, Aji Gautama]]>;
<![CDATA[Abdurohman, Maman]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 4, No 2, May 2019 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v4i2.757
<![CDATA[This paper proposes Accurate Noise Source Detection System using K-Nearest Neighbor (KNN) Method. Noise or sound intensity is usually measured in decibels (dB). In an educational environment the recommended noise index limit is 55 dB. It means that noise louder than that limit is prohibited. While a loud noise in a campus area occurred, it will be troublesome for the authorities to deal with the matter. This paper proposes a noise source detection system that can locate the position of the noise source. This system used Df analog V2 voice sensor for detecting the loud noise intensity. A microcontroller with WiFi capabilities will allow the system to communicate with an Internet of Things (IoT) platform that can perform a learning method to detect the location of the loud noise source. KNN method is used as the learning method. The result shows a user is able to get a warning related to the noise that occurs in an area at once. The precision position of the noise source can be detected with 70% average accuracy rate]]>
<![CDATA[IMPLEMENTASI FUZZY DAN DIJKSTRA PADA SISTEM PENGANGKUTAN SAMPAH ]]>
<![CDATA[Abdillah, Hilal Nabil]]>;
<![CDATA[Rakhmatsyah, Andrian]]>;
<![CDATA[Putrada, Aji Gautama]]>
<![CDATA[ Jurnal Edukasi dan Penelitian Informatika (JEPIN) Vol 5, No 3 (2019): Volume 5 No 3 ]]>
Publisher : <![CDATA[Program Studi Informatika ]]>
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DOI: 10.26418/jp.v5i3.34320
<![CDATA[Tempat sampah tidak dapat diperkirakan kapan penuh atau kosong, mengakibatkan petugas dalam pengangkutannya sering mengunjungi tempat sampah yang kosong dan terkadang petugas sering kembali ke tempat yang kosong tersebut. Tempat sampah yang ada di daerah Perumahan Buah Batu (PBB) merupakan tempat sampah yang dibuat di depan rumah dengan bentuk kubus berdiameter sekitar 60cm x 60cm disertai penutup tempat sampah. Dari permasalahan tersebut dibutuhkan smart monitoring yang dapat menunjukan tempat sampah yang isinya dapat diangkut oleh petugas. Sistem monitoring ini menggunakan sensor ultrasonik dengan output nilai ketinggian sampah dan sensor loadcell dengan ouput nilai berat sampah, dimana nilai output sensor merupakan nilai input untuk Fuzzy, setelah sistem diteruskan dengan sistem Dijkstra. Fuzzy menghasilkan nilai keputusan dari output sensor, hasil fuzzy menjadi penentu tempat sampah mana yang diangkut, jika hasil fuzzy lebih dari satu tempat sampah berstatus ?Angkut? dengan nilai berkisar dari 50 - 100, maka node tersebut membentuk sebuah graph. Dalam pengangkutannya menggunakan dijkstra untuk mendapatkan rute yang paling efisien dari node awal ke semua node yang ada. Sistem terus mengulangi proses pembaruan nilai dan membandingkannya sampai seluruh node selesai. Sehingga sistem mengeluarkan hasil bobot semua node pada graph, berdasarkan nilai bobot yang dihasilkan dibuat list untuk menentukan jalur pengangkutan sampah. Pengujian ini dilakukan hanya dengan menggunakan 5 titik tempat sampah atau disebut juga node yang ada di Perumaha Buah Batu (PBB) sebagai sampel percobaan, node yang dipilih merupakan area penduduk terbanyak di daerah perumahan tersebut. Dalam pengujian pada penelitian ini menghasilkan graph yang dibentuk berdasarkan hasil fuzzy yang berstatus ?Angkut? berjumlah semua node, rute yang dibentuk Gerbang ? G ? I ? H ? E ? C dengan jarak sejauh 1096 meter dan hasil graph yang dibentuk hanya dengan tiga node yakni node C, node I dan node E menghasilkan rute Gerbang ? C ? E ? H dengan jarak 961.]]>
<![CDATA[Increasing The Precision Of Noise Source Detection System using KNN Method ]]>
<![CDATA[Nando, Parlin]]>;
<![CDATA[Putrada, Aji Gautama]]>;
<![CDATA[Abdurohman, Maman]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 4, No 2, May 2019 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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Full PDF (504.591 KB)
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DOI: 10.22219/kinetik.v4i2.757
<![CDATA[This paper proposes Accurate Noise Source Detection System using K-Nearest Neighbor (KNN) Method. Noise or sound intensity is usually measured in decibels (dB). In an educational environment the recommended noise index limit is 55 dB. It means that noise louder than that limit is prohibited. While a loud noise in a campus area occurred, it will be troublesome for the authorities to deal with the matter. This paper proposes a noise source detection system that can locate the position of the noise source. This system used Df analog V2 voice sensor for detecting the loud noise intensity. A microcontroller with WiFi capabilities will allow the system to communicate with an Internet of Things (IoT) platform that can perform a learning method to detect the location of the loud noise source. KNN method is used as the learning method. The result shows a user is able to get a warning related to the noise that occurs in an area at once. The precision position of the noise source can be detected with 70% average accuracy rate]]>
<![CDATA[Performance Improvement of Non Invasive Blood Glucose Measuring System With Near Infra Red Using Artificial Neural Networks ]]>
<![CDATA[Pamungkas, Rizaldi Ramdlani]]>;
<![CDATA[Putrada, Aji Gautama]]>;
<![CDATA[Abdurohman, Maman]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 4, No 4, November 2019 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v4i4.844
<![CDATA[Measurement of body blood sugar levels is one of the important things to do to reduce the number of people with diabetes mellitus. Non-invasive measurement techniques become a blood sugar measurement technique that is more practical when compared to invasive techniques, but this technique has not shown too high levels of accuracy, specificity and sensitivity. For this reason, the non-invasive measurement model using NIR and ANN is proposed to improve the performance of non-invasive gauges. Non-invasive blood sugar measuring devices will be built using a nodemcu board with photodiaodes and NIR transmitters whose data is then processed using ANN models compared to invasive blood sugar data obtained from 40 data. 40 data obtained then used as raw data to build ANN models which 75% percent of it use as training data and 25% od it will be use as testing data to validate accuration of the model been built, the split of data doing randomly without any interference from programmer or model designer. All the data gathered are data collected from all volunteers which willingly to test their blood glucose using invasive glucose meter and non invasive glucose meter which been built. The invasive glucose meter used to gather raw data of blood glucose is SafeAccu-2 with 95% level of accuracy so the accuracy and error parameter calculated in this research are based on that 95% level accurcy of the invasive device.]]>
<![CDATA[Evaluation of IoT-Based Grow Light Automation on Hydroponic Plant Growth ]]>
<![CDATA[Yuda Prasetia]]>;
<![CDATA[Aji Gautama Putrada]]>;
<![CDATA[Andrian Rakhmatsyah]]>
<![CDATA[ Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 2 (2021): August ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.26555/jiteki.v7i2.21424
<![CDATA[This research aims to design, create, and evaluate a hydroponic automation system by monitoring the quality of plant growth that uses LED grow lights and natural light conditions on hydroponics. Checking whether the proposed system has a significant effect on the box Choy hydroponic growth is also an important aspect and becomes the contribution of this paper. The contribution of this paper is by discussing in detail the automation of LED grow lights using RTC modules and relays while also discussing the significance of LED light performance in hydroponic growth. On the proposed hydroponic automation systems, light-feeding is done automatically, this can be carried out with the help of a real-time clock (RTC) module and relays. Furthermore, the monitoring function is carried out through temperature and humidity measurement sensors. The data obtained from the sensor will be stored in the database for research on plant quality. The results of a comparison test show that the LED grows lights are superior in terms of fresh weight, the number of leaves, and plant height respectively with an average value of 23.6 grams, 11.2 leaves, and 18.1 cm on the 30th day. Compared to sunlight, respectively with an average value of 20.2 grams, 9.3 leaves, and 17.1 cm on the 30th day. PDF calculation and t-test are used to calculate the growth significance. The results are that the H0 for fresh weight and leaf growth rate is rejected and the H0 for plant growth rate is not rejected. It can be concluded that the LED grow lights give a significant effect on the fresh weight and leaf growth rate of IoT-based box Choy hydroponics if compared to sunlight.]]>
<![CDATA[Perancangan Software PDU Encoder dan PDU Decoder untuk Layer MAC WiMAX ]]>
<![CDATA[Aji Gautama Putrada]]>
<![CDATA[ Indonesia Journal on Computing (Indo-JC) Vol. 1 No. 1 (2016): March, 2016 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.21108/INDOJC.2016.1.1.34
<![CDATA[WiMAX, singkatan dari Wireless Interoperable Microwave Access merupakan teknologi telekomunikasi dengan sistem wireless broadband access. Teknologi ini dibuat berdasarkan Standar Wireless Metropolitan Area Network IEEE 802.16-2004 dan bekerja pada Media Access Control (MAC) Layer dan Physical (PHY) Layer pada jaringan komputer. MAC dari WiMAX merupakan Layer yang connection oriented dan mengatur akses sebuah perangkat WiMAX pada jaringan Physical. Layer ini mengerjakan berbagai macam hal seperti connection management, penjadwalan serta ARQ dan lain sebagainya. PDU Encoder digunakan untuk membentuk data keluaran dari MAC menuju transmitter Physical. Sebaliknya PDU Decoder membentuk data keluaran MAC dari receiver Physical. Makalah ini membahas tentang perancangan dan implementasi Software PDU Encoder dan PDU Decoder. PDU Encoder dan Decoder dapat melakukan berbagai macam fungsi, antara lain, packing, fragmentation, kalkulasi CRC, dan pemberian MAC Header.]]>
<![CDATA[Improving Smart Lighting with Activity Recognition Using Hierarchical Hidden Markov Model ]]>
<![CDATA[Nur Ghaniaviyanto Ramadhan]]>;
<![CDATA[Aji Gautama Putrada]]>;
<![CDATA[Maman Abdurohman]]>
<![CDATA[ Indonesia Journal on Computing (Indo-JC) Vol. 4 No. 2 (2019): September, 2019 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.34818/INDOJC.2019.4.2.307
<![CDATA[This paper has the aim of implementing the smart lighting systems that is able to analyze daily movement activities, analyze the performance of hierarchical hidden markov models as predictions and analyze the performance of smart lighting with activity analysis using hierarchical hidden markov models. The purpose is to answer the problems that occur, namely the smart lights only turn on if users are right under the lights so users need a smart light which is able to read the movement of people when approaching the lamp or not. Secondly, there are also smart lights, but when usersare under the lights, it only lights up for a few seconds which should light up if there is a person below or a radius around the lamp so that a smart light is needed when someone is underneath and the lights will die it is outside the radius around the lamp. The model used is the hierarchical hidden markov model which is an extension of the hidden markov model which can solve the problem of evaluation, conclusion and learning with the algorithm used is the viterbi algorithm. The result obtained using HHMM are accuracy of 93%, 92% recall and 86% precision.]]>
<![CDATA[Smart Packaging Machine dengan Menggunakan Teknologi Internet of Things (IoT) Berbasis Fuzzy Logic untuk Penghematan Daya Listrik ]]>
<![CDATA[Hanifa Zahra Dhiah]]>;
<![CDATA[Novian Anggis Suwastika]]>;
<![CDATA[Aji Gautama Putrada]]>
<![CDATA[ Indonesia Journal on Computing (Indo-JC) Vol. 4 No. 2 (2019): September, 2019 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.34818/INDOJC.2019.4.2.318
<![CDATA[Sekali produksi dalam keadaan normal tanpa ada kendala apapun, daya listrik yang terpakai oleh mesin packaging adalah 1200-1800 watt, jika terjadi kendala pada saat produksi akan memakan daya listrik lebih dari itu. Penyebab terjadinya pemakaian daya listrik yang berlebih adalah, ketika stok makanan ataupun stok plastik telah habis, mesin tetap berjalan, dan hal tersebut tidak bisa ditangani secara langsung, karena mesin packaging ini tidak memiliki kemampuan untuk mematikan mesin secara otomatis ketika stok makanan telah habis, juga tidak adanya orang yang memonitoring hal tersebut secaralangsung dikarenakan banyaknya mesin yang beroperasi ketika waktu produksi berlangsung. Dampak dari hal tersebut, salah satu yang paling utamanya adalah borosnya konsumsi energi listrik pada saat sekali produksi, dan hal tersebut tidak baik bagi keuangan sebuah industri dan juga tidak baik bagi lingkungan. Tujuan dari pembuataan paper ini untuk mengurangi permasalahan penghematan konsumsi energi listrik dengan dibuatnya sistem yang menggunakan teknologi Internet of Things (IoT) dengan berbasis fuzzy logic untuk penghematan daya listrik, analisis Fuzzy logic digunakan untuk mengolah inputan daya listrik yang di hasilkan mesin, yang diperuntukkan mengontrol kinerja mesin. Hasil yang didapatkan dengan menggunakan metode fuzzy logic serta teknologi IoT sebagai kontrol otomatis adalah produktivitas mesin menjadi lebih hemat 80.15% dari biasanya.]]>
<![CDATA[Smart Packaging Machine (SPANE) berbasis Fuzzy Logic pada Jaringan Internet Of Things (IoT) untuk Optimasi Packing Berat Makanan ]]>
<![CDATA[Taufik Suyanto]]>;
<![CDATA[Novian Anggis Suwastika]]>;
<![CDATA[Aji Gautama Putrada]]>
<![CDATA[ Indonesia Journal on Computing (Indo-JC) Vol. 4 No. 2 (2019): September, 2019 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.34818/INDOJC.2019.4.2.320
<![CDATA[Salah satu permasalahan optimasi kemasan makanan adalah ketidaktepatan berat kemasan makanan. Kasus ketidaktepatan berat kemasan makanan ini sering terjadi pada sistem kemasan makanan konvensional yang sepenuhnya mengandalkan pada perhitungan timbangan digital tanpa proses pengecekan berat secara berulang. Untuk mengatasi kasus ketidaktepatan berat kemasan makanan tersebut diperlukan pengawasan yang terus menerus serta sistem yang mampu melakukan perhitungan dan memberikan keputusan yang tepat pada berat makanan yang sudah dikemas. Internet of Things mampu memberikan kemampuan untuk melakukan pengawasan otomatis secara terus menerus. Sementara fuzzy logic memberikan kemampuan untuk memberikan keputusan terhadap ketepatan berat kemasan makanan. Pada penelitian ini, dibangun smart packaging machine (SPANE) yaitu sistem timbangan digital berbasis IoT dan fuzzy untuk meningkatkan optimasi kemasan makanan. Tahap pertama, pengambilan data dari alat penghitung beban konvensional dan digital. Tahap kedua, data timbangan di analisa dengan kontrol fuzzy logic. Tahap ketiga, ditentukan hasil optimasi yang lebih baik dari kedua alat penghitung beban. Hasil pengujian yang dilakukan didapatkan hasil pada timbangan konvensional 96.24% sedangkan pada SPANE 98.50%. Optimasi kemasan makanan dapat ditingkatkan hingga 3%.]]>
