cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota semarang,
Jawa tengah
INDONESIA
Jurnal Teknik Sipil Unika Soegijapranata : G-SMART (Geoteknik, Struktur, Manajemen Konstruksi, Sumber Daya Air, Transfortasi)
Published by Universitas Katolik Soegijapranata
ISSN : 26205297     EISSN : 26205297     DOI : -
Core Subject : Engineering,
Civil Engineering, Building, Construction & Architecture Engineering Transportation
Jurnal G - SMART : Jurnal Teknik Sipil Unika Soegijapranata yang meliputi Geoteknik, Struktur, Manajemen Konstruksi, Sumber Daya Air dan Transportasi.
Arjuna Subject : -
Articles 96 Documents
 5   6   7   8   9 
Pengaruh Bahan Tambah Anti Retak Terhadap Kuat Tekan Dan Terjadinya Retak Pada Mortar Abram Arry Purwadana; Rendra Willy Saputra; Yohanes Yuli Mulyanto; David Widianto
G-SMART Vol 6, No 1: Juni 2022
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v6i1.3374

Abstract

Monplas adalah zat admixture yang berfungsi untuk mencegah retak pada beton, berupa bubuk yang berbeda dengan zat admixture lainnya yang biasanya berupa cairan. Monplas yang ditambahkan dalam campuran mortar atau beton, ternyata dapat mengurangi porositas beton yaitu perbandingan volume  pori-pori  (volume  yang  ditempati  oleh  air)  terhadap  volume  total  beton. Penambahan Monplas juga dapat meningkatkan  daya  rekat  antara  pasta  semen dengan  agregat dan mempermudah pengerjaan plesteran pada dinding. Meningkatnya daya rekat antara pasta semen dengan agregat akan meningkatkan kuat tekan beton. Salah satu bahan penyusun mortar adalah pasir. Pasir yang digunakan dalam penelitian kali ini adalah Pasir Ambarawa, yang memiliki kandungan lumpur cukup tinggi. Dengan penambahan zat anti retak (Monplas) ini diharapkan dapat mengurangi pengaruh buruk lumpur terhadap mortar. Pengujian yang dilakukan penelitian ini adalah analisis keretakan plat mortar dengan ukuran 25 cm × 25 cm × 2 cm dan 25 cm × 25 cm × 4 cm, serta uji kuat tekan kubus mortar dengan ukuran 5 cm × 5 cm × 5 cm. Jumlah benda uji dari analisis keretakan plat mortar adalah 16 buah dan jumlah benda uji dari uji kuat tekan kubus mortar adalah 24 buah. Penelitian ini bertujuan untuk mengetahui pengaruh zat anti retak (Monplas) terhadap terjadinya retak pada plat mortar akibat pengeringan dan adanya lumpur dan untuk mengetahui kuat tekan kubus mortar yang maksimal akibat penambahan zat anti retak (Monplas).
Analisa Debit Puncak Menggunakan Pendekatan Metode Hidrograf Satuan Sintetis (HSS) Snyder dan HEC-HMS (Studi Kasus: DAS Silandak, Kota Semarang) Monika Indriyani; Rahma Shafira Amalia; Budi Santosa; Daniel Hartanto
G-SMART Vol 6, No 1: Juni 2022
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v6i1.4031

Abstract

Lahan memiliki peran penting pada kehidupan manusia karena dapat digunakan sebagai tempat aktivitas manusia seperti mencari nafkah dan tempat pemukiman. Kebutuhan pengunaan lahan di perkotaan menyebabkan ketersediaan lahan menjadi terbatas. Jika permasalahan ini dibiarkan maka akan terjadi permasalahan penurunan pada Daerah Aliran Sungai (DAS). Salah satunya berada di Kota Semarang yaitu Sungai Silandak.Permasalahan perubahan tata guna lahan menunjukkan perlu diadakannya penelitian analisis debit puncak di Sungai Silandak. Analisis debit puncak dapat menggunakan pendekatan metode Hidrograf Satuan Sintetis (HSS) Snyder, kemudian dilakukan pemodelan menggunakan program HEC-HMS. Penelitian ini juga melakukan prediksi hidrograf DAS Silandak pada Tahun 2045.Perbandingan debit puncak Sungai Silandak metode HSS Snyder dengan program HEC-HMS memiliki hasil selisih debit puncak maksimum sebesar 1,78%. Perubahan penggunaan lahan DAS Silandak dengan asumsi secara linear. Sungai Silandak akan mengalami kenaikan debit yang terjadi pada tahun 2045 sebesar 8,86% sampai 17,22%.
Studi Pengaruh Variasi Kadar Air Terhadap Immediate Settlement Fondasi Dangkal dengan Model Fondasi Telapak Persegi (Studi Kasus Desa Kalikayen, Kecamatan Ungaran Timur, Kabupaten Semarang) Ramon Yanuar Wibisono; Kevin Anggoro Putro; Budi Setiadi; Yohanes Yuli Mulyanto
G-SMART Vol 6, No 1: Juni 2022
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v6i1.3364

Abstract

Desa Kalikayen merupakan salah satu desa yang terletak di Kecamatan Ungaran Timur Kabupaten Semarang Provinsi Jawa Tengah. Desa kalikayen memiliki penduduk dengan jumlah 1306 kepala keluarga. Desa kalikayen terdapat kurang lebih 300 rumah tinggal sederhana, atau sekitar 23 persen rumah yang mengalami kerusakan yang diakibatkan oleh karakteristik tanah di desa tersebut. Kegagalan dan kerusakan pada suatu konstruksi dapat dipengaruhi oleh berbagai macam faktor. Sumber kegagalan dalam konstruksi seringkali dipengaruhi oleh faktor alam dan perilaku manusia. Tujuan dilakukannya penilitian ini adalah menguji karakteristik tanah yang terdapat di Desa Kalikayen, dan mengukur penurunan fondasi dangkal pelat setempat akibat memiliki kadar air tanah berbeda. Pengujian yang dilakukan berupa uji analisis saringan dan hidrometer, uji indeks properties, uji atterberg limit, uji analisis sandcone, uji proctor standard, uji direct shear, dan uji dynamic cone penetrometer. Pengujian analisis prosentase butiran tanah dengan uji saringan dan hidrometer pada tanah di Desa Kalikayen digolongkan tanah yang bergradasi buruk dengan didominasi dengan jenis tanah berlanau (silt). Pengujian Atterberg Limit, berdasarkan USCS, hasil pengujian klasifikasi dan karakteristik tanah di Desa Kalikayen digolongkan dalam klasifikasi MH yaitu lanau tak organik dengan potensi plastisitas pengembangannya yang rendah. Pengujian kuat geser tanah di Desa Kalikayen memiliki tekstur lembut dan karakteristiknya dapat dibentuk oleh tekanan jari yang ringan dan tergolong dalam tanah lempung padat. Pengujian DCP mendapatkan hasil jenis tanah dengan tingkat kepadatan yang buruk (poor). Penurunan terjadi tiap penambahan kadar air, dalam penurunan tanahnya tidak terjadi pengembangan tanah dan penurunan yang terjadi dalam skala model termasuk penurunan yang sangat besarKata Kunci: Tanah Ekspansif, Karakteristik Tanah, Daya Dukung Tanah, Kadar Air, Desa Kalikayen
Kajian Potensi Sedimentasi Pada Waduk Jatibarang Dengan Pemodelan SWAT (Soil and Water Assesment Tool) Han Lois Herlambang; Montana Raisya Putri; Budi Santosa; Djoko Suwarno
G-SMART Vol 6, No 1: Juni 2022
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v6i1.3130

Abstract

Sedimentasi menjadi salah satu permasalahan dalam pengelolaan waduk. Sedimen yang mengendap pada waduk akan mempengaruhi kapasitas tampungan mati dan umur waduk tersebut. Tujuan dari penelitian yaitu menghitung besaran potensi sedimentasi dan hubungan volume sedimentasi dengan umur perkiraan Waduk Jatibarang. Hasil pemodelan Soil and Water Assesment Tool (SWAT) menunjukan potensi volume sedimentasi dari Mei 2014 sampai Desember 2019 mencapai 1.361.811,915 m3 dan Maret 2034 diprediksi tampungan mati pada Waduk Jatibarang akan penuh. Hubungan volume sedimen (x) dengan umur perkiraan Waduk Jatibarang (y) berdasarkan grafik regresi yaitu y = -0,1215x3 + 81,822x2 + 71490.img src="data:image/png;base64,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
Pengaruh Kasus Pandemi Virus Covid-19 Terhadap Kinerja Trans Semarang Megawati Takary Putri; Joshua TH Panggabean; Djoko Setijowarno; Djoko Suwarno
G-SMART Vol 6, No 1: Juni 2022
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v6i1.3480

Abstract

Tugas akhir ini merupakan pengamatan pengaruh kasus pandemi virus covid-19 terhadap kinerja Trans Semarang studi kasus pada Koridor II Terminal Terboyo-Sisemut Ungaran. Metode yang digunakan untuk mendapatkan data primer adalah pengamatan atau survey lapangan dan penyebaran kuesioner. Sedangkan untuk data pembanding atau data sekunder diperoleh dari BLU UPTD Trans Semarang dan dari jurnal-jurnal atau sumber referensi lainnya. Hasil analisa waktu tempuh aktual rata-rata bus Trans Semarang rute Sisemut-Terboyo adalah 1 jam 22 menit dan rute Terboyo-Sisemut adalah 1 jam 25 menit. Selisih waktu kedatangan antar bus (Headway) rata-rata untuk rute Sisemut-Terboyo yaitu 6,62 menit  sedangkan rute Terboyo-Sisemut 6,75 menit. Frekuensi kendaraan yang lewat dalam 1 jam berdasarkan rata rata headway aktual untuk rute Sisemut-Terboyo yaitu 9 kendaraan sedangkan rute Terboyo-Sisemut yaitu 10 kendaraan. Load factor tertinggi Bus Trans Semarang Koridor II  per ruas halte untuk rute Sisemut-Terboyo adalah 105,88 sedangkan untuk rute Terboyo-Sisemut adalah 105,88. Tingkat kepuasan penumpang pada Bus Trans Semarang Koridor II di masa pandemi Covid-19 adalah sudah memuaskan karena kepuasan berada pada kuadran II pertahankan prestasi.
Pengaruh Penambahan Abu Sekam Padi Dan Cairan X Terhadap Kuat Tekan Mortar Edoardus Satria Wicaksana Putra; Ade Eka Prayoga; David Widianto; Budi Setiadi
G-SMART Vol 6, No 1: Juni 2022
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v6i1.3192

Abstract

Mortar merupakan hasil campuran dari semen, agregat halus, dan air. Menurut SNI 03-6825-2002 menyatakan bahwa, mortar didefinisikan sebagai campuran material yang terdiri dari agregat halus (pasir), bahan perekat (tanah liat, kapur, semen portland composite) dan air dengan komposisi tertentu. Tujuan penelitian ini untuk mengetahui pengaruh bahan tambah abu sekam padi dan cairan x terhadap peningkatan kuat tekan mortar dan waktu pekerjaan pembuatan mortar. Serta untuk mengetahui perbandingan optimal penambahan abu sekam padi dan terdapatnya kandungan lumpur sebesar 10% terhadap nilai kuat tekan mortar dan waktu pekerjaan pembuatan mortar.Pada penelitian ini dilakukan beberapa pengujian material yang bertujuan untuk mendapatkan hasil kualitas material yang sesuai dengan standar aturan SNI yang berlaku. Pada pelaksanaan pembuatan benda uji mortar ini menggunakan pedoman SNI 03-6825-2002. Bahan tambah yang digunakan dalam pembuatan benda uji yaitu abu sekam padi dan cairan x yang termasuk bahan tambah tipe E yang memiliki fungsi ganda sebagai water reducing serta accelerating admixture. Dalam penelitian ini total jumlah benda uji sebanyak 96 benda uji.Hasil kuat tekan rata-rata benda uji menunjukkan bahwa nilai kuat tekan rata-rata maksimum di hasilkan oleh mortar dengan komposisi penambahan cairan x sebesar 0,5 % yaitu sebesar 22 MPa, untuk kuat tekan mortar minum didapat dengan komposisi penambahan abu sekam padi sebesar 15% dan lumpur sebesar 10% yaitu sebesar 6 MPa dan kuat tekan normal yaitu sebesar 12 MPa. Pada pengujian kuat tekan lanjutan untuk mortar umur 2 bulan mengalami sedikit penurunan nilai kuat tekan yaitu sebesar 12,6 MPa untuk kuat tekan maksimum dan 6,8 MPa untuk kuat tekan minimum pengujian kuat tekan lanjutan saat mortar berumur 2 bulan bertujuan untuk mengetahui mutu mortar setelah umur 28 hari dengan penambahan cairan x akan mengalami penurunan atau tidak tidak karena cairan x termasuk bahan tambah (admixture) tipe E yang kemungkinan akan mengalami penurunan nilai kuat tekan setelah mortar berumur lebih dari 28 hari. Hasil ini menunjukkan bahwa bahan tambah (admixture) yang dipakai pada penelitian ini dapat mempengaruhi nilai kuat tekan mortar. Kata kunci: Mortar normal, mortar dengan bahan tambah (admixture) abu sekam padi dan cairan x, kuat tekan, mortar
Interpretasi Hasil CPTu Untuk Menghitung Penurunan Konsolidasi Primer dan Daya Dukung Pondasi Dangkal Pada Tanah Lunak Kevin Kristiadi; Clifford Charmen Wijaya; Maria Wahyuni; Rinda Karlinasari
G-SMART Vol 6, No 2: Desember 2022
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v6i2.4809

Abstract

Proyek Perumahan Mutiara Arteri yang terletak di Jalan Gajah Raya, Kecamatan Gayam Sari, Kota Semarang merupakan lokasi penelitian. Pemilihan lokasi berdasarkan pertimbangan bahwa tanah tersebut dahulunya merupakan sawah dan rawa-rawa dengan kondisi tanah yang kurang layak dijadikan lokasi pembangunan. Hal ini disebabkan kondisi tanah yang dilunakkan dan jenuh air sehingga berdampak adanya perubahan kondisi tanah menjadi tanah lunak. Tujuan dari penelitian yaitu untuk mengetahui karakterisitik tanah di lokasi penelitian, jangka waktu dan besar penurunan konsolidasi primer, daya dukung pondasi pada lokasi penelitian sebelum dan sesudah konsolidasi berdasarkan hasil uji CPTu dengan menggunakan program PLAXIS. Dalam penelitian ini pengujian tanah dibagi menjadi 2 kondisi yaitu dengan muka air pada kedalaman 2 meter (muka air asli) dan kedalaman 1,5 meter (saat kondisi hujan). Hasil penelitian ini menunjukan bahwa karakterisitik tanah di lokasi penelitian pada kedalaman 0 – 2 meter merupakan tanah timbunan, 5 – 9 meter merupakan tanah pasir, sedangkan pada kedalaman 9 meter merupakan tanah lempung berlanau dengan konsistensi soft to medium. Dengan kondisi muka air tanah pada kedalaman 2 meter sehingga waktu yang dibutuhkan untuk mengalami konsolidasi 50% (U50) adalah 1856 hari dengan penurunan lokal sebesar 64,07 cm, dan penurunan pondasi sebesar 4,65 cm. Saat terkonsolidasi 90% (U90), sehingga waktu yang dibutuhkan sebesar 3919 hari dengan penurunan lokal sebesar 70,96 cm, dan penurunan pondasi sebesar 6,3 cm. Saat terkonsolidasi 100% (U100), waktu yang dibutuhkan sebesar 6333 hari dengan penurunan lokal sebesar 72,21 cm, dan penurunan pondasi sebesar 6,74 cm. Saat sebelum dikonsolidasi besar daya dukung ijin sebesar 73 kN/m2, daya daya dukung ultimate sebesar 270 kN/m2 dan faktor kemanan sebesar 3,6; sedangkan setelah di konsolidasi besar daya dukung ijin sebesar 88 kN/m2, daya daya dukung ultimate sebesar 300 kN/m2 dan faktor kemanan sebesar 3,4. Pada kondisi muka air tanah pada kedalaman 1,5 meter  waktu yang dibutuhkan untuk mengalami konsolidasi 50% (U50) adalah 1877 hari dengan penurunan lokal sebesar 63,35 cm, dan penurunan pondasi sebesar 5 cm. Saat terkonsolidasi 90% (U90), waktu yang dibutuhkan sebesar 4009 hari dengan penurunan lokal sebesar 71,57 cm, dan penurunan pondasi sebesar 6,97 cm. Saat terkonsolidasi 100% (U100), waktu yang dibutuhkan sebesar 6385 hari dengan penurunan lokal sebesar 73,02 cm, dan penurunan pondasi sebesar 6,74 cm. Saat sebelum dikonsolidasi besar daya dukung ijin sebesar 50 kN/m2, daya daya dukung ultimit sebesar 248 kN/m2 dan faktor kemanan sebesar 4,96; sedangkan setelah di konsolidasi besar daya dukung ijin sebesar 75 kN/m2, daya daya dukung ultimate sebesar 290 kN/m2 dan faktor kemanan sebesar 3,8. Semakin tinggi muka air tanah beban ijin akan turun, dan jangka waktu konsolidasi akan semakin lama.
Analisis Pengaruh Lingkungan Asam Terhadap Beton Bertulang Ditinjau Dari Corrosion Rate Tulangan Iqbaal Rizky Ananto; Fiona Indah Yurisaputri Martanni; Djoko Suwarno; Widija Suseno
G-SMART Vol 6, No 2: Desember 2022
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v6i2.4331

Abstract

Jumlah penduduk di dunia meningkat setiap tahunnya. Pada provinsi Jawa Tengah jumlah penduduk pada tahun 2020 mencapai 36.516.035 juta jiwa. Perkembangan jumlah penduduk menyebabkan kebutuhan manusia meningkat. Guna memenuhi kebutuhan tersebut,maka aktivitas industri dan transportasi dilakukan. Aktivitas tersebut menghasilkan gas emisi yang kemudian menjadi polutan. Zat polutan tersebut akan berkumpul di udara dan menyebabkan terjadinya hujan asam. Hujan asam merupakan proses turunnya air yang bersifat asam dan memiliki pH di bawah 5,6. Kota Semarang dari tahun 2018 hingga tahun 2020 memiliki rata-rata pH air hujan sebesar 5,37. Dengan demikian dapat dikatakan bahwa hujan yang terjadi di kota Semarang adalah hujan asam. Penelitian ini dilakukan untuk mengetahui pengaruh hujan asam terhadap sifat mekanis beton melalui uji kuat tekan beton dan durabilitas beton bertulang melalui uji laju korosi.Hasil uji kuat tekan beton yang di­-curing dan direndam menggunakan air PDAM pada umur 28 hari adalah sebesar 30,93 MPa, sedangkan beton yang di-curing dan direndam menggunakan air pH 5±0,5 pada umur 28 hari adalah sebesar 23,66 MPa.Hasil uji laju korosi pada beton yang di­-curing dan direndam menggunakan air PDAM adalah sebesar 0,218 mm/tahun, sedangkan beton yang di-curing dan direndam menggunakan air pH 5±0,5 memiliki nilai laju korosi sebesar 0,343 mm/tahun.
Efek Penggunaan Shear Wall Terhadap Perilaku Dan Level Kinerja Struktur Gedung Pabrik X (The Effect of Shear Wall Usage on Behavior and Performance Based Design of The Structure of Factory Building X) Lita Lahita; Vinsensia Dina Septiana; David Widianto; Yohanes Yuli Mulyanto
G-SMART Vol 6, No 2: Desember 2022
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v6i2.5075

Abstract

Tasikmalaya, Jawa Barat merupakan daerah yang rawan terjadi gempa. Oleh karena itu dalam perancangan bangunan perlu memperhitungkan gaya akibat gempa. Penggunaan shear wall mampu mengoptimalkan kinerja struktur dengan gaya gempa. Gedung Pabrik X didesain menggunakan shear wall agar dapat diketahui perilaku dan level kinerja strukturnya apabila terkena gaya gempa kemudian dibandingkan dengan gedung Pabrik X tanpa shear wall. Dalam memperhitungkan gaya gempa menggunakan analisis respon spektrum dan analisis pushover untuk level kinerja struktur. Berdasarkan hasil pemodelan, didapatkan hasil perilaku struktur berupa periode gedung Pabrik X tanpa shear wall untuk arah X sebesar 1,699 detik dan arah Y sebesar 1,805 detik sedangkan gedung Pabrik X dengan shear wall sebesar 1,289 detik dan arah Y sebesar 1,419 detik. Displacement pada gedung Pabrik X tanpa shear wall untuk arah X sebesar 229,775 mm dan arah Y sebesar 412,260 mm sedangkan gedung Pabrik X dengan shear wall untuk arah X sebesar 104,959 mm dan arah Y sebesar 61,171 mm. Berdasarkan analisis pushover didapatkan level kinerja struktur gedung Pabrik X tanpa shear wall berupa collapse prevention pada arah X maupun arah Y sedangkan gedung Pabrik X dengan shear wall berupa Immediate Occupancy pada arah X dan Operational pada arah Y menurut peraturan FEMA 440.
Analisis Tingkat Bahaya Erosi Lahan Di Daerah Aliran Sungai (Das) Kupang Menggunakan Metode Modified Universal Soil Loss Equation (Musle) Aldian Seputra Sudianto; Aditya Manung Pratama; Daniel Hartanto; Budi Santosa
G-SMART Vol 6, No 2: Desember 2022
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v6i2.4388

Abstract

Penelitian ini dilakukan dengan tujuan untuk mengetahui laju erosi yang terjadi pada DAS Kupang, Jawa Tengah. Proses erosi yang terjadi perlu dilakukan analisis menggunakan rumus MUSLE (Modified Universal Soil Loss Equation) dengan aplikasi ArcGIS. Metode penelitian ini digunakan untuk mengetahui tingkat bahaya erosi pada area DAS Kupang yang dapat ditampilkan dalam bentuk peta. Hasil penelitian yang diperoleh berupa total laju erosi DAS Kupang sebesar 63.720,681 ton/ha/th kriteria sangat berat dengan luasan DAS sebesar 181,109 km2. Erosi terbesar terjadi pada Kecamatan Talun dengan 28.784,349 ton/ha/th dengan luas kecamatan sebesar 44,821 km2Adapun skala prioritas untuk penanganan bahaya erosi yakni pada Kecamatan Pekalongan Barat, Kota Pekalongan dengan total skor 36,339, Kecamatan Talun, Kabupaten Pekalongan dengan total skor 32,578, dan Kecamatan Petungkriyono, Kabupaten Pekalongan dengan total skor 25,857

Page 7 of 10 | Total Record : 96

 5   6   7   8   9 

Filter by Year

2017 2024


Reset
Filter By Issues
All Issue Vol 8, No 2: Desember 2024 Vol 8, No 1: Juni 2024 Vol 7, No 2: Desember 2023 Vol 7, No 1: Juni 2023 Vol 6, No 2: Desember 2022 Vol 6, No 1: Juni 2022 Vol 5, No 2: Desember 2021 Vol 5, No 1: Juni 2021 Vol 4, No 2: Desember 2020 Vol 4, No 1: Juni 2020 Vol 3, No 2: Desember 2019 Vol 3, No 1: Juni 2019 Vol 2, No 2: Desember 2018 Vol 2, No 1: Juni 2018 Vol 1, No 2: Desember 2017 Vol 1, No 1: Juni 2017 More Issue