Yunarto Yunarto, Yunarto
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DEVELOPING OF TRIGRS (TRANSIENT RAINFALL INFILTRATION AND GRID-BASED REGIONAL SLOPE–STABILITY ANALYSIS) INTO TRIGRS MAP FOR LANDSLIDE SUSCEPTIBILITY MAPPING Yunarto, Yunarto
GEOMATIKA Vol 22, No 1 (2016)
Publisher : Badan Informasi Geospasial in Partnership with MAPIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1496.127 KB) | DOI: 10.24895/JIG.2016.22-1.569

Abstract

ABSTRACTTRIGRS is a modeling program for slope stability against the occurrence of landslide and pore water pressure changes due to infiltration of rainfall. There are two problems in TRIGRS operation. Firstly, whole data must have no mistake before being executed in TRIGRS. Secondly, TRIGRS is not completed by spatial visualization based on Geographic Information System (GIS), so it needs GIS e.g. MapInfo, ArcGIS, and ILWIS to visualize its result. The purpose of this paper is present the result of development of TRIGRS MAP by using integrated mapping technique between MapInfo and Visual Basic. Implementation of TRIGRS MAP for Bandung Regency area has generated a landslide susceptibility map of the area. By using TRIGRS MAP, user can avoid a mistake in data initialization and directly visualize its result as a map. Thus, TRIGRS MAP can be used to process data from other area for creating the landslide susceptibility map more easily and efficiently 
RELOKASI PENDUDUK TERDAMPAK BANJIR/ROB DI KOTA SEMARANG Yunarto, Yunarto; Sari, Anggun Mayang
MAJALAH ILMIAH GLOBE Vol 19, No 2 (2017)
Publisher : Badan Informasi Geospasial

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1618.339 KB) | DOI: 10.24895/MIG.2017.19-2.624

Abstract

ABSTRAKKota Semarang sering dilanda banjir, baik banjir harian akibat rob ataupun banjir sungai yang datang tiap musim hujan. Banjir sungai ataupun rob dapat  menimbulkan genangan di kawasan pantai, terutama kawasan permukiman penduduk dan perkantoran yang berpengaruh pada kerusakan pondasi, lantai, dan dinding rumah/bangunan. Kerugian akibat banjir/rob, di antaranya penduduk harus mengeluarkan biaya untuk meninggikan lantai setiap 2-3 tahun, serta biaya untuk menyambung dinding dan atap rumah setiap 10-15 tahun. Kondisi ini masih berlangsung hingga sekarang. Penelitian ini merekomendasikan agar penduduk di kawasan permukiman tersebut perlu direlokasi ke tempat lebih aman, dengan dibangunnya rumah susun (Rusun) yang bebas banjir dan aman dari longsor. Penelitian ini menggunakan analisis tumpangsusun (overlay) Sistem Informasi Geografis (SIG) pada peta tata guna lahan, peta sebaran penduduk, peta kawasan banjir, dan peta penurunan tanah untuk menghasilkan jumlah penduduk terpapar di dalam zona banjir/rob per kecamatan di kawasan pantai. Kemudian direncanakan jumlah Rusun yang dibutuhkan untuk menampung penduduk pada zona banjir/rob tersebut per kecamatan. Berdasarkan hasil analisis spasial, dapat disimpulkan bahwa penduduk yang terpapar di zona banjir/rob sebanyak 395.877 jiwa dimana pada Kecamatan Semarang Utara memiliki jumlah penduduk terpapar paling tinggi sebesar 111.096 jiwa. Sementara jumlah penduduk terpapar paling rendah adalah Kecamatan Tugu sebesar 15.755 jiwa. Dibutuhkan Rusun sebanyak 1.100 unit yang membutuhkan lahan berupa tanah kosong, tegalan, dan sawah seluas 15.853 km2. Kata kunci: banjir, banjir rob, analisis SIG, relokasi, rumah susun ABSTRACT Semarang is a city in Indonesia that seriously prone to flood, both daily flood due to high tidal flood and river flood due to high rainfall intensity. Both tidal flood and river flood may cause inundation in the seashore area, mostly in a residential area and office building, which affected the building foundation, floor, and wall of the structure. The damage loss due to tidal flood is quite high, for instead, people need to reconstruct the settlements by raising the floor every 2–3 years and the wall including the roof every 10–15 years. This condition is continuing to this day. This research recommends the resident to relocate to the safer areas by constructing flats which are safe from flood and landslide threatens. This research used overlay analysis of Geographical Information System (GIS) on the land use map, population distribution map, flood zone map, and subsidence zone maps to provide the number of the population exposed for each district in flood area. Then the general overview planning of the flats needed to accommodate the population in the flood area per district. The spatial analysis showed that the exposed population in the tidal flood zone are 395,877 people where Semarang Utara district has the highest population exposed by the flood about 111.096 people. Meanwhile, the lowest number of the exposed people is in Tugu Sub-district as many as 15,755 people. The flats needed about 1.100 units and take an area of 15,853 km2 which is in the form of the vacant area, moor, or rice fields. Keywords: river flood, tidal flood, GIS analysis, relocation, flats
Comparison Algorithm Backpropagation And Support Vector Machine On The Introduction of Corn Seed Type Yunarto, Yunarto; Pribadi, Muhammad Rizky; Irsyad, Hafiz
Jurnal Algoritme Vol 1 No 1 (2020): Oktober 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1022.778 KB) | DOI: 10.35957/algoritme.v1i1.539

Abstract

Jagung termasuk tumbuhan biji-bijian seperti padi, gandum, sorgum yang dikonsumsisebagai sumber makanan pokok di Amerika dan beberapa wilayah di Indonesia seperti Madura,Nusa Tenggara Timur, Sulawesi dan Jawa Tengah. Jagung biji memiliki banyak jenis, makadari itulah jika jagung biji tersebut tercampur akan susah untuk dibedakan. Tujuan daripenelitian ini adalah untuk mengenali biji jagung tersebut. Jenis biji jagung yang digunakanadalah jagung merah pozole, jagung pipil, jagung putih dan jagung warna-warni yang difotomenggunakan camera 16MP dengan jarak pengambilan foto 10cm antara kamera dengan objekjagung. Metode pengenalan yang digunakan adalah algoritma backpropagation dan support vector machine, sedangkan untuk ekstraksi fitur menggunakan metode GLCM(Gray Co-occurence Matrix) yang terdiri dari Contrast, energy, homogeneity, dan correlation. Pada perhitungan dengan confusion matrix hasil tertinggi didapatkan pada algoritmabackpropagation dengan rata-rata accuracy 97,5, rata-rata precision 95% dan rata-rata recallsebesar 95,1% dibandingkan dengan algoritma support vector machine yang hanya mendapatrata-rata accuracy 97,1%, rata-rata precision 93,3% dan rata-rata recall sebesar 95%.