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RANCANG BANGUN SISTEM INFORMASI BATAM DIRECTORY MENGGUNAKAN METODE BACKWARD CHAINING BERBASIS MOBILE hamsir, Hamsir; Nurcahyo, Gunadi W; Defit, Sarjon
Elektron : Jurnal Ilmiah Vol 4 No 2 (2012): Elektron Jurnal Ilmiah
Publisher : Teknik Elektro Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1004.268 KB) | DOI: 10.30630/eji.4.2.30

Abstract

This article is designed for an information system in the form of expert system applications to present information on Batam Directory. The purpose of this system is to help provide information about the city of Batam as a whole to the residents of Batam city in particular and thelocal and foreign tourists as well as prospective investors in general. The system presents information in the form of public service to the residents of Batam city government and other newcomers as well as products and services are made ​​and offered by the business and government. The analysis was done by determining the first goal, then do these arching to obtain the desired information. The design system uses backward chaining inference method to the implementation ofthe system using My-SQL database systems and programming languages​​ of PHP and JQuery. The system is based on mobile, so it can be accessed using a mobile device.
IMPLEMENTASI MOVING AVERAGE FILTER PADA MIKROKONTROLER SEBAGAI PEREDAM NOISE SENSOR PIEZO ELEKTRIK UNTUK MENDETEKSI GELOMBANG SEISMIK (GEMPA BUMI) Zulharbi, Zulharbi; Firdaus, Firdaus; Antonisfia, Yul; Defit, Sarjon
Prosiding Semnastek PROSIDING SEMNASTEK 2014
Publisher : Universitas Muhammadiyah Jakarta

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Abstract

Getaran  akibat  gempa  bumi    akan  mengakibatkan  adanya  frekuensi  gelombang  seismik  denganfrekuensi  rendah (0Hz - 20Hz),  untuk  mendeteksi  keberadaan  frekuensi  gelombang  seismiktersebut  dapat  menggunakan  sensor  piezo  elektrik.  Piezo  elektrik  adalah  sebuah  sensor  seismikyang  mempunyai   getaran gempa  beramplitudo rendah  dan sangat mudah terkontaminasi noisesehingga  dibutuhkan  filter  untuk  meredam  sinyal  noise  tersebut.  Moving  Average  (MA)  filteradalah  suatu  metode  yang  sederhana  dan  berguna  untuk  menapis  derau  acak  yang  terdapat  padaderau asli. MA filter bekerja dengan cara meratakan sejumlah titik tertentu dari isyarat masukanuntuk  menghasilkan  tiap  titik  dari  isyarat  luaran. Gelombang  seismic    (getaran buatan) padapenelitian ini adalah dengan memberikan amplitudo sensor piezo PVDF antara  3mm, 5mm, 7mm,9mm dan 12mm pada frekuensi 2 Hz (konstan). Sensor piezo mendeteksi kekuatan getaran buatandengan  menggunakan  Moving  Average  Filter  yang  menghasilkan    nilai SNR  (signal  to  noiseratio)  lebih  kecil  dibandingkan  tidak  menggunakan  MAF  Nilai  PGA  (peak  groundacceleration)  dalam  satuan  grafitasi akan  tinggi pada  saat  sinyal  amplitude  getaran  yangdiberikan  juga  tinggi  (PGA  = 0,01G  pada  saat  amplitude  getaran  3mm  dan  1,43G  pada  saatamplitude getaran 12 mm).
SISTEM PAKAR PENENTUAN BAKAT ANAK DENGAN MENGGUNAKAN METODE FORWARD CHAINING Salisah, Febi Nur; Lidya, Leony; Defit, Sarjon
Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Vol 1, No 1 (2015): Februari
Publisher : Department of Information System of UIN SUSKA Riau

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Abstract

Saat ini masih banyak orang tua yang belum mengetahui bakat pada anak mereka. Sedikitnya jumlah pakar untuk berkonsultasi merupakan salah satu penyebab hal ini. Penelitian ini menggunakan sistem pakar untuk mengatasi permasalahan tersebut. Sistem pakar akan memindahkan kemampuan pakar tersebut ke dalam komputer. Bakat-bakat yang digunakan dalam penelitian ini adalah bakat anak menurut standar USOE America. Untuk mesin inferensi penelitian ini menggunakan forward chaining. Anak-anak yang diidentifikasi bakatnya adalah anak TK usia 4-6 tahun.  Hasil analisa menunjukan bahwa sistem pakar ini membutuh 27 indikator, 83 variabel dan 33 rule. Berdasarkan hasil percobaan, sistem pakar ini berhasil mengidentifkasi bakat anak.
Pengembangan Sistem Keamanan Jaringan Komputer Melalui Perumusan Aturan (Rule) Snort untuk Mencegah Serangan Synflood Sahrun, Nori; Roestam, Rusdianto; Defit, Sarjon
SATIN - Sains dan Teknologi Informasi Vol 1, No 2 (2015)
Publisher : STMIK Amik Riau

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Abstract

Rule  snort  merupakan  database  yang  berisi  polapola  serangan  signature  jenis  serangan  yang  disusun sesuai dengan perintah-perintah snort. Rule snort ini, harus di update secara rutin supaya ketika ada sesuatu teknik  serangan  yang  baru  maka  serangan  tersebut dapat  terdeteksi,  dan  program  dalam  penelitian  ini yang  akan  mengupdate  rule  snort  tersebut  dalam mencegah serangan SYNflood. Dalam penulisan rule snort terdapat aturan-aturan yang harus di ikuti yaitu pertama  rule  snort  harus  ditulis  dalam  satu  baris  ( single line), dan yang  kedua snort terbagi menjadi dua bagian yaitu rule header dan rule option. Rule header berisi  tentang  rule  action,  protocol,  source  dan destination IP address,netmask,  source dan destination port.  Rule  option  berisi  alert  message  dan  berbagai dan  berbagai  informasi  dimana  seharusnya  paket tersebut  diletakkan.  Dalam  pengembangan  keamanan jaringan sangat penting untuk di rumuskan  seranganserangan  yang  akan  mengakibatkan  system  down dapat diatasi oleh rule terbaru
Pemilihan Supplier Obat yang tepat dengan Metode Simple Additive Weighting Trimulia, Cyntia; Defit, Sarjon; Nurcahyo, Gunadi Widi
Jurnal Sains, Teknologi dan Industri Vol 16, No 1 (2018): DESEMBER 2018
Publisher : Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v16i1.6735

Abstract

Apotek adalah perusahaan yang bergerak di bidang farmasi berupa obat-obatan. Obat-obatan bersumber dari beberapa supplier. Dengan banyaknya supplier menyebabkan sulit dalam menentukan supplier yang bagus. Untuk menentukan supplier yang baik, maka dibutuhkan sebuah sistem pengambilan keputusan. Metode yang digunakan untuk mengambil keputusan dalam penelitian ini adalah Simple Additive Weighting (SAW). Data yang diolah berupa data-data kualitas, harga, petunjuk kegunaan, garansi, pemesanan, pemenuhan pesanan, dan pelayanan. Penilaian dapat dikembangan dengan kriteria yang akan di jadikan acuan untuk perengkingan supplier yang ada. Penilaian dari masing-masing kriteria diperoleh dari penilaian pemilik Apotek Mama Jakarta itu sendiri. Dengan adanya pemilihan supplier nantinya akan memudahan untuk membandingkan hasil kinerja supplier. Hasil yang didapatkan dengan menggunakan metode ini mampu mendapatkan supplier terbaik. Pengambilan keputusan ini membantu pemilik apotek dalam melakukan evaluasi terhadap supplier.
Perbandingan Algoritma K-Means Clustering dengan Fuzzy C-Means Dalam Mengukur Tingkat Kepuasan Terhadap Televisi Dakwah Surau TV Malik, Rio Andika; Defit, Sarjon; Yuhandri, Yuhandri
RABIT Vol 3 No 1 (2018): Januari
Publisher : RABIT

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (573.499 KB)

Abstract

Dawah Television Surau TV is a broadcasting media that presents broadcasts around Islam. This media will quickly develop as it presents broadcasting material in meeting the spiritual needs of its viewers. To Increased media development is highly dependent on the satisfaction of the audience in all aspects of broadcast supporting. It is therefore, to measure the level of audience satisfaction as an effort to generate continuous broadcast quality improvement.This research is performing of algorithm clustering comparation with K-Means Clustering modeling and Fuzzy C-Means modeling to classify and mapping the most appropriate dataset so that it can assist analysing or measuring the level of audience satisfaction toward the dawah television Surau TV. Comparison of clustering algorithm performance with K-Means Clustering modeling and Fuzzy C-Means modeling is based on processing speed and trace value of each RMSE parameter of clustering algorithm. The RMSE result of clustering research using algorithm with K-Means Clustering is 2.09879 and by using algorithm with Fuzzy C-Means model is 2.07911. Fuzzy C-Means modeling speed is faster in conducting the clustering process compared with K-Means Clustering modeling. It can be concluded that clustering with Fuzzy C-Means modeling is able to produce more accurate cluster compared to clustering with K-Means Clustering modeling accuracy   Keywords: Clustering; K-Means; Fuzzy C-Means; Satisfaction rate survey; RMSE
Classification of Pineapple Fruit Comosus Merr (Nanas) Quality Using Learning Vector Quantization Method Efendi, Muhamad; Defit, Sarjon; Nurcahyo, Gunadi Widi
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 1 (2021): March 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v2i2.4621

Abstract

The demands of publics for these fruits Ananas Comosus Merr (Pineapple) became higher years to years because of the fruit has so many virtues for human healthy and the taste of this fruit is sweet and fresh. Therefore the pineapple farmers have to protect the quality and quantity of this plant in order to get high produce. This research help the pineapple farmers to classify to quality of pineapple fruits by using neural network with Learning Vector Quantization method which has 2 classes, such as: First quality (1st) and Second quality (2nd) quality. This method has 2 process they are : training process and testing process. To input data in the training and testing process are using uniformity, characteristic of varieties, the rate of aging, hardness, size, stem, crown, manure, destroyer, spoilage, rotten and the total solid content of the least was taken by observed the crop of pineapple farmers in the Teluk Batil village Sungai Apit district Siak Riau province. Learning Vector Quantization method automatically will classify the pineapple into their class. The result of the testing classification has gotten the accuracy 65.56% for the first (1st) quality and 34.44% for the second (2nd) quality. At the second testing has gotten 66.67% the accuracy for the first (1st) quality and 33.33% for the second (2nd) quality. At the third (3rd) testing has gotten 64.44% the accuracy for first (1st) quality and 35.56% for the second (2nd) quality.
Penentuan Mutu Kelapa Sawit Menggunakan Metode K-Means Clustering Am, Andri Nofiar; Defit, Sarjon; Sumijan, Sumijan
Jurnal KomtekInfo Vol 5 No 3 (2018): KOMTEKINFO
Publisher : Lembaga Penelitian Dan Pengabdian Masyarakat UPI-YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29165/komtekinfo.v5i3.159

Abstract

The classification of the quality of palm oil in PT Tasma Puja is still done by laboratory testing and then the data is saved manually in Excel. The method of grouping takes time and allows data to be lost. With the development of knowledge, it can be replaced by a data mining approach that can be used to classify the quality of palm oil based on its standards. The k-Means clustering method can be applied to classify the quality of palm oil based on water, dirt and free fatty acids. The data used is the quality data of palm oil in December 2017 as many as 31 data with criteria of good, very good and not good. The test results contained 3 clusters, namely cluster 0 for good categories amounted to 12 data, cluster 1 for very good category amounted to 13 data and cluster 2 for less good categories amounted to 6 data. The k-Means clustering method can be used for data processing using the concept of data mining in grouping data according to criteria.
Indeks Kesiapan Perguruan Tinggi dalam Mengimplementasikan Smart Campus Zakir, Supratman; Defit, Sarjon; Vitriani, Vitriani
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6, No 3: Juni 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3504.759 KB) | DOI: 10.25126/jtiik.201963986

Abstract

Keberhasilan perguruan tinggi memanfaatkan Teknologi Informasi dan Komunikasi (TIK) atau sering dikenal dengan istilah smart campus sebagai upaya kompetitif dan bernilai saing pada hakekatnya terletak pada sejumlah indicator seperti technoware, infoware, orgaware dan humanware. Upaya pencapaaian tujuan penggunaan smart campus tersebut dibutuhkan skema perencanaan yang matang dan need analysis yang menyeluruh. Banyak perguruan tinggi yang gagal mengimplementasikn smart campus disebabkan beberapa hal, seperti perencanaan yang tidak baik, tenaga ahli yang tidak siap, sarana prasarana yang kurang memadai, biaya awal pengembangan yang tidak tersedia dan kebijakan yang tidak konsisten. Penelitian ini bertujuan untuk melihat kesiapan perguruan tinggi dalam mengimplemetasikan smart campus. Penelitian ini menggunakan jenis penelitian deskriptif kuantitatif. Hasil penelitian memperlihatkan bahwa pengembangan cyber campus pada komponen ICT Use yang mencakup dimensi kebutuhan dan keselarasan serta dimensi proses dan tata kelola baru memasuki tahap kurang siap. Komponen ICT Readiness yang mencakup dimensi sumber daya teknologi pada kategori hampir berhasil. Komponen ICT Capability yang mencakup dimensi komunitas memasuki kategori belum berhasil. Komponen ICT impact sudah memasuki kategori hampir berhasil. Secara keseluruhan komponen pengembangan cyber campus dikategorikan hampir berhasilAbstractThe triumph of higher education in utilizing ICT or commonly termed as smart campus as a competitive strategy depends on some indicator like technoware, infoware, orgaware and human ware. To achieve such goal is not an easy task, it needs to have a good planning and a holistic needs analysis. Many educational institutions fail to implement smart campus due to some factors for instance bad planning, unready human resources, unsporting infrastructure, lack of fund and inconsistent policy.  The research aims at revealing readiness index of higher education in utilizing ICT. The research uses descriptive quantitative approach research that describes an object as it is with research stages including presurvey, leterature studies, questionnaires, data analysis and empirical findings. Data were obtained by using a research questionnaire. The finding reveals that developing cyber campus in the component ICT Use which covers need, harmony, and process dimensions and governance is categorized less-ready. Component of ICT Readiness which covers dimension of technology resources is categorized as successful, meanwhile component of ICT capability of community dimension is not successful yet. Component of ICT impact comes to category near to successful. As a whole, the components of developing cyber campus are in the success category.        
Perbandingan Algoritma K-Means Clustering dengan Fuzzy C-Means Dalam Mengukur Tingkat Kepuasan Terhadap Televisi Dakwah Surau TV Malik, Rio Andika; Defit, Sarjon; Yuhandri, Yuhandri
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 3 No 1 (2018): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (573.499 KB) | DOI: 10.36341/rabit.v3i1.387

Abstract

Da'wah Television Surau TV is a broadcasting media that presents broadcasts around Islam. This media will quickly develop as it presents broadcasting material in meeting the spiritual needs of its viewers. To Increased media development is highly dependent on the satisfaction of the audience in all aspects of broadcast supporting. It is therefore, to measure the level of audience satisfaction as an effort to generate continuous broadcast quality improvement.This research is performing of algorithm clustering comparation with K-Means Clustering modeling and Fuzzy C-Means modeling to classify and mapping the most appropriate dataset so that it can assist analysing or measuring the level of audience satisfaction toward the da'wah television Surau TV. Comparison of clustering algorithm performance with K-Means Clustering modeling and Fuzzy C-Means modeling is based on processing speed and trace value of each RMSE parameter of clustering algorithm. The RMSE result of clustering research using algorithm with K-Means Clustering is 2.09879 and by using algorithm with Fuzzy C-Means model is 2.07911. Fuzzy C-Means modeling speed is faster in conducting the clustering process compared with K-Means Clustering modeling. It can be concluded that clustering with Fuzzy C-Means modeling is able to produce more accurate cluster compared to clustering with K-Means Clustering modeling accuracy Keywords: Clustering; K-Means; Fuzzy C-Means; Satisfaction rate survey; RMSE
Co-Authors Adi Gunawan, Adi Aditiya, Rahmad Agus Perdana Windarto Ahmadi Ahmadi Aktavera, Beni Alfikrizal, Khaliq Alturky, Shaza Am, Andri Nofiar Andema, Henky Ardian, Deno Yulfa Arif Budiman Arista, Ruly Dwi Ayunda, Afifah Trista Azim, Fauzan Bastola, Ramesh Bosker Sinaga Bufra, Fanny Septiani Dari, Rahmatia Wulan Dina Mardiati, Dina Efendi, Muhamad Elda, Yusma Erwis, Fauzi Fakhri, Dwiki Aulia Fatimah, Noor Febi Nur Salisah, Febi Nur Febrina, Yerri Kurnia Ferdinal, Dendi Firdaus Firdaus Fitriani, Yetti Frinosta, Ewif Guslendra, Guslendra Hamsir Hamsir Hariona, Popi Hartati, Yuli Hayati, Nova Hendri, Halifia Hidayad, Asri Ihksan, Muhammad Iswavigra, Dwi Utari JERI WANDANA Juansen, Monsya Juledi, Angga Putra Juliansa, Hengki Julius Santony Juwita Z, Arika Kareem, Shahab Wahhab Khair, Fuad El Kurniawan, Jefdy Kurniawan, Mhd Hary Leony Lidya, Leony Lestari, Yuni Indah Malik, Rio Andika Malik, Rio Andika Mardayatmi, Suci Mardison Mardison Mardison Meilinda Sari Miftahul Hasanah, Miftahul Muhammad, L. J. Mulyana Putra, Beni Na’am, Jufriadif Nas, Chairun Nori Sahrun, Nori Nurcahyo, Gunadi W Nurcahyo, Gunadi Widi Nurcahyo, Gunadi Widi Nurdin, Yogi K Nurhidayat Oktarian, Suhefi Pati, Muhammad Ibnu Perdana Windarto, Agus Perdana, Daeng Saputra Purnomo, Nopi Putra, Rahman Arief Putri, Adek Rafiska, Rian Rahmiyanti, R Ramadhanu, Agung Rani, Larissa Navia Rian Kurniawan Riandari, Fristi Roza, Faisal Rusdianto Roestam Said, Abdul Azis Salam, Riyan Ikhbal Sari, Laynita Septiano, Renil Sharon Sintia, Sintia Sirait, Weri Siregar, Fajri Marindra Sovia, Rini Sri Dewi Sumijan Sumijan Sumijan Sumijan, S Suryani, Vivi Susandri, Susandri Syahputra, M Syaljumairi, Raemon Syawitri, Afriosa Syofneri, Nandel Theodorus, Daniel Triandini, Melissa Trimulia, Cyntia Virgo, Ismail Vitriani, Vitriani Wahyuni, Ritna Wanto, Anjar Yudha Aditya Fiandra, Yudha Aditya Yuhandri Yuhandri, Yuhandri Yuhandri, Y Yul Antonisfia Yunus, Yuhandri Zaimy, Mike Zakir, Supratman Zulharbi Zulharbi Zulrahmadi Zulvitri, Z