Articles
<![CDATA[Klasifikasi Kualitas Pisau Potong Tembakau (CUT CELL) Menggunakan Metode Radial Basis Function (RBF) ]]>
<![CDATA[Apriyanto, Fungki]]>;
<![CDATA[Sujono, Hari Agus]]>;
<![CDATA[Hermanto, Luky Agus]]>
<![CDATA[ INTEGER: Journal of Information Technology Vol 1, No 2 (2016): September 2016 ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya ]]>
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DOI: 10.31284/j.integer.2016.v1i2.62
<![CDATA[Indonesia is one of countries that produces several types of tobacco. Almost 80% tobacco produces is used of cigarette industry. Tobacco leaves slicing into small cuts is one of the process of cigarette production. The cutting process of tobacco requires Cut Cell which is able to cut tobacco into small pieces. Contol is required in the process of making cut cell to set the quality of the blade. The quality control often has problem in determining the Cut Cell quality. The problem is the length of time needed in determining the quality. In this fast paced era, the Quality Control is demanded to be able to determine the cut cell quality quickly and accurately. To support this need from the Quality Control, a system that can be used to determine the cut cell quality which has fast output result. The research process is started with collecting the system needs, followed by system designing, then system making, and system test. The system designing is initiated by preparing the test data and training data which are going to be used for the making and testing of the system. RADIAL BASIS FUNCTION consist of several calculation processes. The first process is the process of center search of each variable using K-MEANS method. Aftar the center is found, the deviation standard of each variable is calculated. The second process is setting the GAUSSIAN matrix of every group found. The third process is the process of new weight and bias values search by doing pseudo-inverse GAUSSIAN matrix multiplication. The forth process is classification in which this process sets out the classication result by multiplying the value of GAUSSIAN matrix and new weight and bias applying network output formula. The experiment done to 75 experiment data which are compared to manual data as the reference result 12 different data, thus it can be concluded that the accuracy level of this system is 84 %.]]>
<![CDATA[Asthma Identification Using Gas Sensors and Support Vector Machine ]]>
<![CDATA[Hari Agus Sujono]]>;
<![CDATA[Muhammad Rivai]]>;
<![CDATA[Muhammad Amin]]>
<![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.v16i4.8281
<![CDATA[The exhaled breath analysis is a procedure of measuring several types of gases that aim to identify various diseases in the human body. The purpose of this study is to analyze the gases contained in the exhaled breath in order to recognize healthy and asthma subjects with varying severity. An electronic nose consisting of seven gas sensors equipped with the Support Vector Machine classification method is used to analyze the gases to determine the patient's condition. Non-linear binary classification is used to identify healthy and asthma subjects, whereas the multiclass classification is applied to recognize the subjects of asthma with different severity. The result of this study showed that the system provided a low accuracy to distinguish the subjects of asthma with varying severity. This system can only differentiate between partially controlled and uncontrolled asthma subjects with good accuracy. However, this system can provide high sensitivity, specificity, and accuracy to distinguish between healthy and asthma subjects. The use of five gas sensors in the electronic nose system has the best accuracy in the classification results of 89.5%. The gases of carbon monoxide, nitric oxide, volatile organic compounds, hydrogen, and carbon dioxide contained in the exhaled breath are the dominant indications as biomarkers of asthma.The performance of electronic nose was highly dependent on the ability of sensor array to analyze gas type in the sample. Therefore, in further study we will employ the sensors having higher sensitivity to detect lower concentration of the marker gases.]]>
<![CDATA[Electronic Nose using Gas Chromatography Column and Quartz Crystal Microbalance ]]>
<![CDATA[Muhammad Rivai]]>;
<![CDATA[Djoko Purwanto]]>;
<![CDATA[Hendro Juwono]]>;
<![CDATA[Hari Agus Sujono]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 9, No 2: August 2011 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v9i2.703
<![CDATA[The conventional electronic nose usually consists of an array of dissimilar chemical sensors such as quartz crystal microbalance (QCM) combined with pattern recognition algorithm such as Neural network. Because of parallel processing, the system needs a huge number of sensors and circuits which may emerge complexity and inter-channel crosstalk problems. In this research, a new type of odor identification which combines between gas chromatography (GC) and electronic nose methods has been developed. The system consists of a GC column and a 10-MHz quartz crystal microbalance sensor producing a unique pattern for an odor in time domain. This method offers advantages of substantially reduced size, interferences and power consumption in comparison to existing odor identification system. Several odors of organic compounds were introduced to evaluate the selectivity of the system. Principle component analysis method was used to visualize the classification of each odor in two-dimensional space. This system could resolve common organic solvents, including molecules of different classes (aromatic from alcohols) as well as those within a particular class (methanol from ethanol) and also fuels (premium from pertamax). The neural network can be taught to recognize the odors tested in the experiment with identification rate of 85 %. It is therefore the system may take the place of human nose, especially for poisonous odor evaluations. ]]>
<![CDATA[Klasifikasi Kualitas Pisau Potong Tembakau (CUT CELL) Menggunakan Metode Radial Basis Function (RBF) ]]>
<![CDATA[Fungki Apriyanto]]>;
<![CDATA[Hari Agus Sujono]]>;
<![CDATA[Luky Agus Hermanto]]>
<![CDATA[ INTEGER: Journal of Information Technology Vol 1, No 2 (2016): September 2016 ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya ]]>
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DOI: 10.31284/j.integer.2016.v1i2.62
<![CDATA[Indonesia is one of countries that produces several types of tobacco. Almost 80% tobacco produces is used of cigarette industry. Tobacco leaves slicing into small cuts is one of the process of cigarette production. The cutting process of tobacco requires Cut Cell which is able to cut tobacco into small pieces. Contol is required in the process of making cut cell to set the quality of the blade. The quality control often has problem in determining the Cut Cell quality. The problem is the length of time needed in determining the quality. In this fast paced era, the Quality Control is demanded to be able to determine the cut cell quality quickly and accurately. To support this need from the Quality Control, a system that can be used to determine the cut cell quality which has fast output result. The research process is started with collecting the system needs, followed by system designing, then system making, and system test. The system designing is initiated by preparing the test data and training data which are going to be used for the making and testing of the system. RADIAL BASIS FUNCTION consist of several calculation processes. The first process is the process of center search of each variable using K-MEANS method. Aftar the center is found, the deviation standard of each variable is calculated. The second process is setting the GAUSSIAN matrix of every group found. The third process is the process of new weight and bias values search by doing pseudo-inverse GAUSSIAN matrix multiplication. The forth process is classification in which this process sets out the classication result by multiplying the value of GAUSSIAN matrix and new weight and bias applying network output formula. The experiment done to 75 experiment data which are compared to manual data as the reference result 12 different data, thus it can be concluded that the accuracy level of this system is 84 %.]]>
<![CDATA[MONITORING TEMPAT DUDUK DIGEREJA GETSEMANI MENGGUNAKAN SENSOR PROXIMITY DAN LOAD CELL BERBASIS IoT ]]>
<![CDATA[Joshua Noviandy]]>;
<![CDATA[Hari Agus Sujono]]>
<![CDATA[ Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2022: SNESTIK II ]]>
Publisher : <![CDATA[Institut Teknologi Adhi Tama Surabaya ]]>
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DOI: 10.31284/p.snestik.2022.2850
<![CDATA[Perkembangan gereja tidak luput dari banyaknya jumlah jemaat yang semakin banyak contohnya saja gereja GSJA satelit Getsemani. Monitoring tempat duduk (kursi) di Gereja dibangun untuk membantu sarana kegiatan ibadah dengan cara memantau jumlah jemaat yang hadir yang duduk di kursi saat peribadatan. Internet of Thing atau disingkat IoT adalah sebuah paradigma untuk membuat “sesuatu” termasuk peralatan elektronik dapat terhubung dengan dengan system atau aplikasi melalui jaringan internet. Kursi yang dibangun dilengkapi dengan mikrokontroler wemos D1, sensor proximity dan load cell. Sensor ini difungsikan untuk mendeteksi jika ada sesorang dalam jarak 15 cm dari kursi, sedangkan proximity memberitahukan ke microkontroler bawah ada seseorang, jika pengguna duduk load cell akan melakukan pengecekan apakah berat melebihi 5 kg atau tidak, jika berat sudah dipastikan melebihi 5 kg maka microkontroler akan mengirimkan informasi ke website yang berisi status kursi. Dengan internet of thing menghubungkan microcontroller dengan sistem untuk memantau dan menyimpan data jumlah jemaat memudahkan pengurus gereja dalam mengetahui perkembangan banyaknya jemaat tiap bulannya dan memudahkan jemaat dalam mengetahui tempat duduk yang kosong]]>
<![CDATA[Sistem Monitoring Kualitas Air Sungai Berdasarkan Kadar PH dan Kekeruhan Air Berbasis Internet of Things ]]>
<![CDATA[Uinsa Pradana]]>;
<![CDATA[Hari Agus Sujono]]>
<![CDATA[ Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2022: SNESTIK II ]]>
Publisher : <![CDATA[Institut Teknologi Adhi Tama Surabaya ]]>
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DOI: 10.31284/p.snestik.2022.2538
<![CDATA[Air sungai yang merupakan salah satu sumber air bersih dari tahun ke tahun mengalami pencemaran yang disebabkan oleh berbagai limbah. Pada penelitian ini bertujuan untuk memonitoring kualitas air sungai dengan pH dan kekeruhan air sebagai indikatornya. Pengujian pH dan kekeruhan ini diujikan di sungai wonokitri dengan menggunakan sensor pH SEN:0161-V2 dan sensor Turbidity SEN-0189 serta arduino uno yang terhubung ke perangkat laptop dan android berbasis Internet of Things. Pengambilan data dilakukan dengan cara memasang sensor pH, sensor kekeruhan dan arduino di sungai wonokitri untuk memonitoring nilai ph dan Turbidity air pada sungai wonokitri. Pada periode pertama dari 117 data pH dan Turbidity, nilai rata-rata pH sebesar 5,33 dan hanya 50,46% yang memenuhi syarat PERMENKES RI No. 492/2010. Sedangkan nilai rata-rata turbidity sebesar 327,65 dan hanya 31,62% yang memenuhi syarat PERMENKES RI No. 492/2010. Sedangkan pada periode kedua dari 101 data ph dan turbidity, rata-rata nilai pH sebesar 7.335347 dan 100% memenuhi syarat PERMENKES RI No. 492/2010. sedangkan rata-rata nilai turbidity sebesar 579.2129 dan hanya 9% yang memenuhi syarat PERMENKES RI No. 492/2010. Dari penelitian ini, dapat disimpulkan bahwa sungai wonokitri tidak layak untuk dikonsumsi karena nilai turbidity tidak memenuhi syarat PERMENKES RI No.592/2010]]>
<![CDATA[Realtime hybrid offline-online power loss analysis-based Simulink simulation ]]>
<![CDATA[Riny Sulistyowati]]>;
<![CDATA[Ganesha Fikri Prawidya]]>;
<![CDATA[Hari Agus Sujono]]>
<![CDATA[ International Journal of Applied Power Engineering (IJAPE) Vol 12, No 1: March 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijape.v12.i1.pp49-61
<![CDATA[The power distribution system applied in Indonesia is the radial system. The system is considered to be the simplest and most economical. The bad or good distribution of electrical power can be observed from the quality of the distribution of power supplied. Voltage has to be monitored and kept constant. An analysis was conducted at Bendul Merisi Feeder which has 11 buses, to find the value of supplied power also the value of voltage drop of each bus. The Simulink method is used to simulate and analyze active and reactive power at each cluster. Based on the result of the simulation analysis, the average was obtained by adding up electrical power received every hour, then dividing by 10, the number of buses connected to the load. The smallest average of active power supplied to each bus happened at 09.00 a.m., i.e. 112137.94 VA. The biggest value of active power supplied to each bus happened at 1.00 p.m., i.e. 115129.05 VA. The total voltage drop that occurred in the distribution supply was 224 volts or 1.12% out of 20 kV supplied, indicating that the supply of voltage was according to the standardized rule implemented by PLN (State Electricity Company), i.e. the voltage drop should not exceed the maximum of 10%.]]>
<![CDATA[Rancang Bangun Alat Monitoring Suhu dan Kelembaban Untuk Kontrol Penyiraman Pada Area Tanam Hidroponik Berbasis Internet of Things ]]>
<![CDATA[Badruz Zaman]]>;
<![CDATA[Hari Agus Sujono]]>
<![CDATA[ Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2023: SNESTIK III ]]>
Publisher : <![CDATA[Institut Teknologi Adhi Tama Surabaya ]]>
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DOI: 10.31284/p.snestik.2023.4071
<![CDATA[ABSTRAKCapsicum frutescens atau yang lebih sering dikenal sebagai cabai rawit merupakan salah satu jenis buah yang memiliki nilai ekonomi yang tinggi. Sifat cabai rawit yang mudah untuk dibudidayakan di berbagai elevasi (daratan tinggi dan rendah) menjadikan cabai rawit popular sebagai bumbu dapur di Indonesia. Namun tanaman cabai memerlukan pengawasan yang intensif. Maka dari itu dibuat sistem monitoring dengan mikrokontroller NodeMCU, Soil Moisture, dan Sensor DHT22. Apabila data sensor suhu melebihi 38 °C atau nilai kelembaban mencapai 50% maka pompa air akan secara otomatis aktif dan menyiram tanaman cabai. Hasil pengujian alat yaitu monitoring suhu mencapai tingkat akurasi sebesar 99,71% sedangkan monitoring kelembaban tanah mencapai akurasi pada Pot 1 sebesar 97,75% dan pada Pot 2 sebesar 98,58%. Sedangkan hasil pengujian Quality of Service menunjukkan indeks QoS hari pertama sebesar 3,48 dan pada hari kedua sebesar 3,43 dengan peringkat memuaskan.]]>
